Citations - Hivatkozások

Független hivatkozások

Összes független hivatkozás: 182
Ebből  
  Thomson Reuters Web of Knowledge/Web of Science: 56
  Science Direct: 8
  Scopus: 95
  Egyéb: 74

A Thomson Reuters Web of Knowledge/Web of Science adatbázisban, a Science Direct adatbázisban, és a Scopus adatbázisban szereplő hivatkozások

Hivatkozott cikk/Hivatkozó cikk Web of Knowledge Science Direct Scopus
Johanyák Zs. Cs.: Fuzzy szabály-interpolációs módszerek és mintaadatok alapján történő automatikus rendszergenerálás, PhD disszertáció, Hatvany József Informatikai Tudományok Doktori Iskola, Miskolci Egyetem, Miskolc, 2007.      
1 J. Botzheim, L. Gál and L.T. Kóczy: Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms, In: Studies in Computational Intelligence, Recent Advances in Decision Making, Springer, Berlin/Heidelberg, 2009, DOI: 10.1007/978-3-642-02187-9_3, pp. 21-43. X   X
2 A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16. X   X
Johanyák, Z. C.: Student Evaluation Based On Fuzzy Rule Interpolation, International Journal of Artificial Intelligence, Autumn 2010, Vol. 5, No. A10, ISSN 0974-0635, pp. 37-55.      
3 A.-I. Stinean, S. Preitl, R.-E. Precup, C.-A. Dragos, M.-B. Radac: 2-DOF Control Solutions for BLDC-m Drives, Proceedings of IEEE 9th International Symposium on Intelligent Systems and Informatics (SISY 2011), Subotica, Serbia, 2011, pp. 29-34, ISBN: 978-1-4577-1973-8, IEEE Catalog Number: CFP1184C-CDR.     X
4 A. S. Paul, D. Barbulescu, F. Dragan: A Distributed System Architecture for Audio Signal Processing, Proceedings of 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI 2011), Timisoara, Romania, 2011, pp. 137-142, ISBN 978-1-4244-9107-0     X
5 Radac, M.-B., Precup, R.-E., Petriu, E. M. and Preitl, St.: Application of IFT and SPSA to servo system control, IEEE Transactions on Neural Networks, 22 (12 PART 2) , art. no. 6075258 , pp. 2363-2375 , DOI: 10.1109/TNN.2011.2173804, ISSN: 1045-9227 X   X
6 Babu Devasenapati, S., Ramachandran, K.I.: Hybrid fuzzy model based expert system for misfire detection in automobile engines, (2011) International Journal of Artificial Intelligence, 7 (11 A), pp. 47-62.     X
7 C. Pozna, R.-E. Precup: New Results in Abduction Process Modeling, Proceedings of 15th International Conference on Intelligent Engineering Systems (INES 2011), Poprad, Slovakia, 2011, pp. 203-208, ISBN 978-1-4244-8955-8, IEEE Catalog Number: CFP11IES-CDR     X
8 R.-C. David, R.-E. Precup, S. Preitl, J. K. Tar, J. Fodor: Parametric Sensitivity Reduction of PI-Based Control Systems by Means of Evolutionary Optimization Algorithms, Proceedings of 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI 2011), Timisoara, Romania, 2011, pp. 241-246, ISBN 978-1-4244-9107-0     X
9 C. Poznaa,N. Minculetec, R.E. Precup, L.T. Kóczy, Á. Ballagi: Signatures: Definitions,operatorsandapplicationsto fuzzy modelling, Fuzzy Sets and Systems, (2012) , doi: 10.1016/j.fss.2011.12.016   X  
10 R.-E. Precup, M.-L. Tomescu, E. M. Petriu, L.-E. Dragomir: Stable Fuzzy Logic Control of Generalized van der Pol Oscillator, International Journal of Artificial Intelligence, vol. 7, no. A11, pp. 36-46, Oct. 2011, ISSN 0974-0635     X
Johanyák, Z. C.: Sparse Fuzzy Model Identification Matlab Toolbox - RuleMaker Toolbox, IEEE 6th International Conference on Computational Cybernetics, November 27-29, 2008, Stara Lesná, Slovakia, pp. 69-74.      
11 K. Balázs, J. Botzheim, L. Kóczy: Comparative Analysis of Interpolative and Non-interpolative Fuzzy Rule Based Machine Learning Systems Applying Various Numerical Optimization Methods, Proceedings of WCCI 2010 IEEE World Congree on Computational Intelligence, July, 18-23, 2010 - CCIB, Barcelona, Spain, pp. pp. 1 - 8. X   X
12 A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16. X   X
13 Erra, U., Senatore, S.: Fuzzy shape classification exploiting geometrical and moments descriptors, (2011) IEEE International Conference on Fuzzy Systems, art. no. 6007702, pp. 733-740.     X
14 D Vincze, S Kovács: Incremental Rule Base Creation with Fuzzy Rule Interpolation-Based Q-Learning, I.J. Rudas et al. (Eds.):Studies in Computational Intelligence, 2010, Volume 313, Computational Intelligence in Engineering, pp. 191-203 X   X
15 Vincze, D., Kovács, S.: Performance optimization of the fuzzy rule interpolation method "fIVE", (2011) Journal of Advanced Computational Intelligence and Intelligent Informatics, 15 (3), pp. 313-320.     X
16 Kovacs, S.: Fuzzy Rule Interpolation in Embedded Behaviour-based Control, IEEE International Conference on Fuzzy Systems (FUZZ 2011), Taipei, TAIWAN, JUN 27-30, 2011, pp. 436-441. X    
Johanyák, Z. C.: Fuzzy Rule Interpolation based on Subsethood Values, in Proceedings of 2010 IEEE Interenational Conference on Systems Man, and Cybernetics (SMC 2010), 10-13 October 2010, ISBN 978-1-424-6587-3, pp. 2387-2393.      
17 Kovács, L.: Compound distance function for similarity measurement between fuzzy sets, (2011) Journal of Advanced Computational Intelligence and Intelligent Informatics, 15 (3), pp. 299-303.     X
Johanyák, Z. C., Ádámné, A.M.: Fuzzy Modeling of the Relation between Components of Thermoplastic Composites and their Mechanical Properties, Proceedings of the 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009), May 28-29, 2009, Timisoara, Romania, pp. 481-486.      
18 C.A. Dragos, S. Preitl, R.E. Precup: Model Predictive Control Solutions for an Electromagnetic Actuator, Proceedings of 7th International Symposium on Intelligent Systems and Informatics SISY 2009, Subotica (Serbia), ISBN 978-1-4244-5349-8, IEEE Catalog Number: CFP0984C-CDR, 2009, pp. 59 - 64.     X
19 Claudiu Pozna, Radu-Emil Precup, Jozsef K. Tar, Igor Skrjanc, Stefan Preitl: New results in modelling derived from Bayesian filtering, Knowledge-Based Systems, vol. 23, no. 2, pp. 182-194, March 2010.   X X
Johanyák, Z. C., Kovács, S: Fuzzy modeling of Petrophysical Properties Prediction Applying RBE-DSS and LESFRI, International Symposium on Logistics and Industrial Informatics (LINDI 2007), September 13-15, 2007, Wildau, Germany, pp. 87-92.      
20 Pozna, C., Precup, R.-E., Minculete, N., Antonya, C.: Cognition aspects concerning an abstraction model, (2010) Proceedings of the 10th IASTED International Conference on Artificial Intelligence and Applications, AIA 2010, pp. 414-419.     X
21 R.E. Precup, M.L. Tomescu, S. Preitl, E.M. Petriu: Fuzzy Logic-based Stabilization of a Magnetic Ball Suspension System, International Journal of Artificial Intelligence (IJAI), Autumn 2010, Vol. 5, No. A10, ISSN 0974-0635, pp. 56-66     X
22 W.E.-S. Afify and A.H.I. Hassan: Permeability and Porosity Prediction from Wireline logs Using Neuro-Fuzzy Technique, Ozean Journal of Applied Sciences 3(1), 2010, ISSN 1943-2429, pp. 157-175. X    
Johanyák, Z. C., Kovács, S.: Sparse Fuzzy System Generation by Rule Base Extension, 11th IEEE International Conference of Intelligent Engineering Systems (IEEE INES 2007), June 29 - July 1, 2007, Budapest, ISBN 1-4244-1148-3, pp. 99-104      
23 K. Balázs, J. Botzheim, L. Kóczy: Comparative Analysis of Interpolative and Non-interpolative Fuzzy Rule Based Machine Learning Systems Applying Various Numerical Optimization Methods, Proceedings of WCCI 2010 IEEE World Congree on Computational Intelligence, July, 18-23, 2010 - CCIB, Barcelona, Spain, pp. pp. 1 - 8. X   X
24 R.-E. Precup, M.-L. Tomescu, St. Preitl: Fuzzy Logic Control System Stability Analysis Based on Lyapunov's Direct Method, International Journal of Computers, Communications & Control, ISSN 1841-9836, E-ISSN 1841-9844, Vol. IV (2009), No. 4, pp. 415-426. X   X
25 J. Botzheim, L. Gál and L.T. Kóczy: Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms, In: Studies in Computational Intelligence, Recent Advances in Decision Making, Springer, Berlin/Heidelberg, 2009, DOI: 10.1007/978-3-642-02187-9_3, pp. 21-43. X   X
26 A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16. X   X
27 R.E. Precup, S. Preitl, E.M. Petriu, J.K. Tar, M.L. Tomescu, C. Pozna: Generic two-degree-of-freedom linear and fuzzy controllers for integral processes, Journal of The Franklin Institute, vol. 346, no. 10, pp. 988-1003, Dec. 2009. X X X
28 R.-E. Precup, R.-C. David, E. M. Petriu, S. Preitl and A. S. Paul: Gravitational Search Algorithm-Based Tuning of Fuzzy Control Systems with a Reduced Parametric Sensitivity, In: Soft Computing in Industrial Applications, A. Gaspar-Cunha, R. Takahashi, G., Schaefer and L. Costa (Eds.), Advances in Intelligent and Soft Computing, vol. 96, Springer-Verlag, Berlin, Heidelberg, 2011, pp. 141-150, ISBN 978-3-642-20504-0, ISSN 1867-5662. X    
29 R.-E. Precup, M.-B. Rădac, S. Preitl, M.-L. Tomescu, E. M. Petriu, A. S. Paul: IFT-Based PI-Fuzzy Controllers: Signal Processing and Implementation, Proceedings of 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009), Milan, Italy, July 2-5, 2009, ISBN 978-989-8111-99-9, vol. 1 Intelligent Control Systems and Optimization, pp. 207-212. X   X
30 M.B. Rădac, R.E. Precup, E.M. Petriu, S. Preitl, C.A. Dragoş: Iterative Feedback Tuning Approach to a Class of State Feedback-Controlled Servo Systems, Proceedings of 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009), Milan, Italy, July 2-5, 2009, ISBN 978-989-8111-99-9, vol. 1 Intelligent Control Systems and Optimization, pp. 41-48. X   X
31 R.-E. Precup, M.-B. Radac, St. Preitl, E. M. Petriu, C.-A. Dragos: Iterative Feedback Tuning in Linear and Fuzzy Control Systems, "Towards Intelligent Engineering and Information Technology", editors: I. J. Rudas, J. Fodor, J. Kacprzyk, Studies in Computational Intelligence, vol. 243, Springer-Verlag, Berlin, Heidelberg, ISBN 978-3-642-03736-8, ISSN 1860-949X (Print) 1860-9503 (Online), 2009, pp. 179 - 192. X   X
32 M.B. Radac, R.E. Precup, S. Preitl and C.A. Dragos: Iterative Feedback Tuning in MIMO Systems. Signal Processing and Application, 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009), May 28-29, 2009, Timisoara, Romania, pp. 77-82. X   X
33 R.-E. Precup, C. Gavriluta, M.-B. Radac, S. Preitl, C.-A. Dragos, J. K. Tar, E. M. Petriu: Iterative Learning Control Experimental Results for Inverted Pendulum Crane Mode Control, Proceedings of 7th International Symposium on Intelligent Systems and Informatics SISY 2009, Subotica (Serbia), ISBN 978-1-4244-5349-8, IEEE Catalog Number: CFP0984C-CDR, 2009, pp. 323 - 328. X   X
34 C. Pozna, R.E. Precup: Modeling Derived from Bayesian Filtering: Analysis of Estimation Process, Proc. of 13th International Conference on Intelligent Engineering Systems, April 16-18, 2009, Barbados, ISBN 978-1-4244-4113-6, pp. 73-78. X   X
35 C.-A. Dragos, S. Preitl, R.-E. Precup, M. Cretiu and J. Fodor: Modern Control Solutions with Applications in Mechatronic Systems, Computational Intelligence in Engineering, I. J. Rudas, J. Fodor and J. Kacprzyk (Eds.), Springer-Verlag, Berlin, Heidelberg, Studies in Computational Intelligence, vol. 313, pp. 87-102, 2010, ISBN 978-3-642-15219-1, ISSN 1860-949X. X   X
36 C.A. Dragos, S. Preitl, M.B. Radac, R.E. Precup: Nonlinear and Linearized Models and Low-cost Control Solution for an Electromagnetic Actuator, 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009), May 28-29, 2009, Timisoara, Romania, pp. 89-94. X   X
37 R.-E. Precup, R.-C. David, S. Preitl, E. M. Petriu, J. K. Tar: Optimal Control Systems with Reduced Parametric Sensitivity Based on Particle Swarm Optimization and Simulated Annealing, in Intelligent Computational Optimization in Engineering Techniques and Applications, M. Koppen, G. Schaefer and A. Abraham (Editors), Springer-Verlag, Berlin, Heidelberg, 2011, pp. 177-207, ISBN 978-3-642-21704-3, ISSN 1860-949X. X   X
38 M.B. Rădac, R.E. Precup,S. Preitl, E.M. Petriu, C.A. Dragoş, A.S. Paul and S. Kilyeni: Signal Processing Aspects in State Feedback Control Based on Iterative Feedback Tuning, Proceedings of the 2nd conference on Human System Interactions (HSI 2009), Catania, Italy, 2009, ISBN:978-1-4244-3959-1, pp. 37-42. X   X
39 R.-E. Precup, S. Preitl, E.M. Petriu, J.K. Tar, M.-B. Radac, C.-A. Dragos: Stable Design of Fuzzy Controllers for Robotic Telemanipulation Applications, Proc. 2009 IEEE Workshop on Computational Intelligence in Virtual Environments, pp. 1-6, Nashville. TN, USA, April 2009. X   X
Johanyák, Z. C., Kovács, S.: Polar-cut Based Fuzzy Model for Petrophysical Properties Prediction, SCIENTIFIC BULLETIN of “Politehnica” University of Timisoara, ROMANIA, Transactions on AUTOMATIC CONTROL and COMPUTER SCIENCE, ISSN 1224-600X, Vol: 57(67) No: 24/ 2008, pp. 195-200.      
40 A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16. X   X
Johanyák, Z.C. and Kovács, S.: Fuzzy set approximation using polar co-ordinates and linguistic term shifting, 4rd Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence (SAMI 2006), Herl'any, Slovakia, 2006, pp. 219-227.      
41 A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16. X   X
Johanyák, Z.C. and Szabó, A.: Tool life modelling using RBE-DSS method and LESFRI inference mechanism, A GAMF Közleményei, Kecskemét, XXII. (2008), ISSN 0230-6182, pp. 17-28.      
42 A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16. X   X
Johanyák, Z.C., Dr. Kovács Sz: A fuzzy tagsági függvény megválasztásáról, A GAMF Közleményei, Kecskemét, XIX. évfolyam (2004), ISSN 0230-6182, pp. 73-84.      
43 G. Molnárka: Management of Uncertainty in Visual Examination Procedure in Building Diagnostics with Fuzzy Expert System, Proceedings of ISCIII'09 4th International Symposium on Computational Intelligence and Intelligent Informatics, 2009, Luxor, Egypt, pp. 31-40. X    
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-511      
44 A.-I. Stinean, S. Preitl, R.-E. Precup, C.-A. Dragos, M.-B. Radac: 2-DOF Control Solutions for BLDC-m Drives, Proceedings of IEEE 9th International Symposium on Intelligent Systems and Informatics (SISY 2011), Subotica, Serbia, 2011, pp. 29-34, ISBN: 978-1-4577-1973-8, IEEE Catalog Number: CFP1184C-CDR.     X
45 C.-A. Dragos, S. Preitl, R.-E. Precup, E. M. Petriu, A.-I. Stinean: A Comparative Case Study of Position Control Solutions for a Mechatronics Application, Proceedings of 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM2011) Budapest, Hungary, 2011, pp. 814-819, ISBN 978-1-4577-0837-4, IEEE Catalog Number CFP11775-CDR, ISSN 2159-6247. X   X
46 Pozna, C., Kóczy, L.-T., Precup, R.-E., Ballagi, Á.: A kantian pattern of knowledge, the observation representation, (2010) SIISY 2010 - 8th IEEE International Symposium on Intelligent Systems and Informatics, art. no. 5647371, pp. 405-412.     X
47 R.-E. Precup, H. Hellendoorn: A survey on industrial applications of fuzzy control, COMPUTERS IN INDUSTRY Volume: 62 Issue: 3 Pages: 213-226 DOI: 10.1016/j.compind.2010.10.001 Published: APR 2011 X X X
48 R.E. Precup, I. Mosincat, M.B. Radac, S. Preitl, S. Kilyeni, E.M. Petriu, C.A. Dragos: Experiments in Iterative Feedback Tuning for Level Control of Three-Tank System, Proceedings of 15th IEEE Mediterranean Electromechanical Conference MELECON 2010, Valletta, Malta, 2010, pp. 564-569, ISBN 978-1-4244-5794-6, IEEE Catalog number: CFP10MEL-CDR. X   X
49 Perfilieva, I; Wrublova, M; Hodakova, P. : Fuzzy Interpolation According to Fuzzy and Classical Conditions , ACTA POLYTECHNICA HUNGARICA Volume: 7 Issue: 4 Special Issue: SI Pages: 39-55 Published: 2010 X    
50 A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16. X    
51 Erra, U., Senatore, S.: Fuzzy shape classification exploiting geometrical and moments descriptors, (2011) IEEE International Conference on Fuzzy Systems, art. no. 6007702, pp. 733-740.     X
52 Shen, Q., Yang, L.: Generalisation of scale and move transformation-based fuzzy interpolation, (2011) Journal of Advanced Computational Intelligence and Intelligent Informatics, 15 (3), pp. 288-298.     X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-514      
53 C. Pozna, V. Prahovean, R.-E. Precup: A New Pattern of Knowledge Based on Experimenting the Causality Relation, Proceedings of 14th International Conference on Intelligent Engineering Systems INES 2010, Las Palmas of Gran Canaria, Spain, 2010, pp. 61-66, ISBN 978-1-4244-7651-0.     X
Johanyák, Z.C., Kovács, S.: A brief survey and comparison on various interpolation based fuzzy reasoning methods, Acta Polytechnica Hungarica, Vol. 3, No. 1, 2006, ISSN 1785-8860, pp. 91-105.      
54 Lior Shamir : A proposed stereo matching algorithm for noisy sets of color images, Computers & Geosciences ISSN:0098-3004, Volume 33, Issue 8, August 2007, Pages 1052-1063 X   X
55 J. Vascak and L. Madarasz: Adaptation of fuzzy cognitive maps - a comparison study, Acta Polytechnica Hungarica, vol. 7, no. 3, pp. 109-122, 2010. X   X
56 Vaščák, J.: Approaches in Adaptation of Fuzzy Cognitive Maps for Navigation Purposes, Proceedings of 8th IEEE International Symposium on Applied Machine Intelligence and Informatics SAMI 2010, Herl'any, Slovakia, ISBN 978-1-4244-6423-4, pp. 31-36. X   X
57 J. Vašcák and K. Hirota: Integrated Decision-Making System for Robot Soccer, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.15 No.2 March 2011 , pp. 156-163.     X
58 M.-B. Radac, R.-E. Precup, E. M. Petriu, P. A. Ianc, S. Preitl, C.-A. Dragos: Low-Cost Optimal State Feedback Fuzzy Control of Nonlinear Second- Order Servo Systems, Proceedings of 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2011), Ottawa, ON, Canada, 2011, IEEE Catalogue Number: CFP11CIM-CDR, ISBN: 978-1-61284-923-2, pp. 103-106. X   X
59 A.S. Paul, R.E. Precup, C. Pozna, R.C. David: nDSP: A Platform for Audiophile Software Audio Processing, International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), 27-29 May 2010, Timisoara, Romania, pp. 431-436.     X
60 C.A. Dragos, S. Preitl, R.E. Precup, R.G. Bulzan, C. Pozna, J.K. Tar: Takagi-Sugeno Fuzzy Controller for a Magnetic Levitation System Laboratory Equipment, International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), 27-29 May 2010, Timisoara, Romania, pp. 55-60.     X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-533      
61 C.-A. Dragos, S. Preitl, R.-E. Precup, E. M. Petriu, M.- B. Radac, A.-I. Stinean: Alternative control solutions for vehicles with continuously variable transmission. A case study, Proceedings of 15th International Conference on System Theory, Control and Computing (ICSTCC 2011), Sinaia, Romania, 2011, pp. 194-199.     X
Johanyák, Z.C., Kovács, S.: Distance based similarity measures of fuzzy sets, SAMI 2005, 3rd Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence, Herl'any, Slovakia, January 21-22 2005, ISBN 963 7154 35 3, pp. 265-276.      
62 H.H. Huang, Y.H. Kuo: Cross-Lingual Document Representation and Semantic Similarity Measure, A Fuzzy Set and Rough Set Based Approach, IEEE Transactions on Fuzzy Systems, ISSN: 1063-6706, Vol. 18, Issue:6, pp. 1098 - 1111 X   X
63 C.-A. Dragos, S. Preitl, R.-E. Precup, C. S. Nes, E. M. Petriu: Model Predictive Control Solutions for Vehicular Power Train Systems, Bulletin of the Polytehnic Institute of Iasi, Automatic Control and Computer Science Section, vol. 56 (60) no. 4, 2010, pp. 27-40, "Gheorghe Asachi" Technical University of Iasi, Romania, ISSN 1220-2169.     X
64 S. Freitag, W. Graf, M. Kaliske, J.-U. Sickert : Prediction of time-dependent structural behaviour with recurrent neural networks for fuzzy data, COMPUTERS & STRUCTURES Volume: 89 Issue: 21-22 Special Issue: SI Pages: 1971-1981 DOI: 10.1016/j.compstruc.2011.05.013 Published: NOV 2011   X X
65 Freitag, S ; Graf, W; Kaliske, M: Recurrent neural networks for fuzzy data , INTEGRATED COMPUTER-AIDED ENGINEERING Volume: 18 Issue: 3 Pages: 265-280 DOI: 10.3233/ICA-2011-0373 Published: 2011 X   X
66 Graf, W., Freitag, S., Kaliske, M., Sickert, J.U.: Recurrent Neural Networks for Uncertain Time-Dependent Structural Behavior, COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Vol. 25, Issue 5, Special Issue: Sp. Iss. SI, July 2010, ISSN: 1093-9687 , pp. 322-333. X   X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-519      
67 C.-A. Dragos, S. Preitl, R.-E. Precup, R.-G. Bulzan, E. M. Petriu, J. K. Tar: Experiments in Fuzzy Control of a Magnetic Levitation System Laboratory Equipment, Proceedings of 8th IEEE International Symposium on Intelligent Systems and Informatics SISY 2010, Subotica, Serbia, 2010, pp. 601-606, ISBN: 978-1-4244-7395-3, IEEE Catalog Number: CFP1084C-CDR.     X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-511      
68 J. Botzheim, L. Gál and L.T. Kóczy: Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms, In: Studies in Computational Intelligence, Recent Advances in Decision Making, Springer, Berlin/Heidelberg, 2009, DOI: 10.1007/978-3-642-02187-9_3, pp. 21-43.     X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation by the Least Squares Method, 7th International Symposium of Hungarian Researchers on Computational Intelligence (HUCI 2006), November 24-25, 2006 Budapest, ISBN 963 7154 54 X, pp. 495-506.      
69 A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16. X    
Johanyák, Z.C., Kovács, S.: The effect of different fuzzy partition parameterization strategies in gradient descent parameter identification, 4th International Symposium on Applied Computational Intelligence and Informatics (SACI 2007), May 17-18, 2007 Timisoara, Romania, pp. 141-146.      
70 Rădac, M.-B., Precup, R.-E., Preitl, S., Tar, J.K., Fodor, J. and Petriu, E.M.: Gain-Scheduling and Iterative Feedback Tuning of PI Controllers for Longitudinal Slip Control, IEEE 6th International Conference on Computational Cybernetics, November 27-29, 2008, Stara Lesná, Slovakia, pp. 183-188.     X
71 A. S. Paul, R.-E. Precup, J. Fodor and M.-B. Radac: New Experimental Setups for Audio Signal Processing, 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009),May 28–29, 2009, Timişoara, Romania, pp. 405-410. X   X
72 R.C. David, M.B. Rădac, S. Preitl and J.K. Tar: Particle Swarm Optimization-Based Design of Control Systems with Reduced Sensitivity, 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009),May 28–29, 2009, Timişoara, Romania, pp. 491-496. X   X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-530      
73 I. Perfilieva, D. Dubois, H. Prade, F. Estevac, L. Godoc, P. Hodáková: Interpolation of fuzzy data: Analytical approach and overview, Fuzzy SetsandSystems 192 (2012)134–158, doi:10.1016/j.fss.2010.08.005   X  
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-517      
74 C.A. Dragos, S. Preitl, R.E. Precup, D. Pirlea, C.S. Nes, E.M. Petriu, C. Pozna: Modeling of a Vehicle with Continuously Variable Transmission, Proceedings of 19th International Workshop on Robotics in Alpe-Adria-Danube Region RAAD 2010, Budapest, Hungary, 2010, pp. 441-446, IEEE Catalog Number: CFP1075J-CDR, ISBN: 978-1-4244-6884-3.     X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-515      
75 A.S. Paul, R.E. Precup, C. Pozna, R.C. David: nDSP: A Platform for Audiophile Software Audio Processing, International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), 27-29 May 2010, Timisoara, Romania, pp. 431-436.     X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-526      
76 M.-B. Radac, F.-C. Enache, R.-E. Precup, E. M. Petriu, S. Preitl, C.-A. Dragos,: Previous and Current Cycle Learning Approach to a 3D Crane System Laboratory Equipment, Proceedings of 15th International Conference on Intelligent Engineering Systems (INES 2011), Poprad, Slovakia, 2011, pp. 197-202, ISBN 978-1-4244-8955-8, IEEE Catalog Number: CFP11IES-CDR     X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-516      
77 C. Pozna, R.E. Precup, N. Minculete, C. Antonya, C.A. Dragos: Properties of Classes, Subclasses and Objects in an Abstraction Model, Proceedings of 19th International Workshop on Robotics in Alpe-Adria-Danube Region RAAD 2010, Budapest, Hungary, 2010, pp. 291-296, IEEE Catalog Number: CFP1075J-CDR, ISBN: 978-1-4244-6884-3.     X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-532      
78 C. Pozna, R.-E. Precup: Results concerning a new pattern of human knowledge, Proceedings of 2011 2nd International Conference on Cognitive Infocommunications CogInfoCom 2011, Budapest, Hungary, 2011, 18 pp., E-ISBN: 978-963-8111-78-4, Print ISBN: 978-1-4577-1806-9     X
Johanyák, Z.C., Kovács, S.: A brief survey on fuzzy set interpolation methods, Doktoranduszok Fóruma, Miskolci Egyetem, 9 November 2006, pp. 72-77.      
79 Kovács, L.: Rule approximation in metric spaces, (2010) SAMI 2010 - 8th International Symposium on Applied Machine Intelligence and Informatics, Proceedings, art. no. 5423702, pp. 49-52. X   X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-531      
80 A.-I. Stinean, S. Preitl, R.-E. Precup, C. Pozna, C.-A. Dragos, M.-B. Radac: Speed and position control of BLDC servo systems with low inertia, Proceedings of 2011 2nd International Conference on Cognitive Infocommunications CogInfoCom 2011, Budapest, Hungary, 2011, 10 pp., E-ISBN: 978-963-8111-78-4, Print ISBN: 978-1-4577-1806-9     X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-527      
81 R.-E. Precup, E. M. Petriu, C.-A. Dragos, R.-C. David: Stability Analysis Results Concerning the Fuzzy Control of a Class of Nonlinear Time-Varying Systems, Theory and Applications of Mathematics & Computer Science, vol. 1, no. 1, 2011, pp. 2-10, ISSN 2067-2764     X
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-524      
82 S. Preitl, R.-E. Precup, C.-A. Dragos, M.-B. Radac: Tuning of 2-DOF Fuzzy PI(D) Controllers. Laboratory Applications, Proceedings of 11th IEEE International Symposium on Computational Intelligence and Informatics (CINTI 2010), Budapest, Hungary, 2010, pp. 237-242, ISBN 978-1-4244-9278-7, IEEE Catalog Number CFP1024M-PRT.     X
Johanyák, Z.C., Kovács, S.: Sparse Fuzzy System Generation by Rule Base Extension, 11th IEEE International Conference of Intelligent Engineering Systems (IEEE INES 2007), June 29 - July 1, 2007, Budapest, ISBN 1-4244-1148-3, pp. 99-104      
83 Precup Radu-Emil; Tomescu Manius L.; Preitl Stefan; et al.: STABILITY ANALYSIS APPROACH TO A CLASS OF FUZZY CONTROLLED NONLINEAR TIME-VARYING SYSTEMS , EUROCON 2009: INTERNATIONAL IEEE CONFERENCE DEVOTED TO THE 150 ANNIVERSARY OF ALEXANDER S. POPOV, St. Petersburg, RUSSIA Date: MAY 18-23, 2009, VOLS 1- 4, pp. 958-963 X    
Johanyák, Z.C., Parthiban, R, and Sekaran, G.: Fuzzy Modeling for an Anaerobic Tapered Fluidized Bed Reactor, SCIENTIFIC BULLETIN of “Politehnica” University of Timisoara, ROMANIA, Transactions on AUTOMATIC CONTROL and COMPUTER SCIENCE, ISSN 1224-600X, Vol: 52(66) No: 2 / 2007, pp. 67-72.      
84 D. Vincze, S. Kovács: Performance issues of the implemented FRI 'FIVE', 11th International Symposium on Computational Intelligence and Informatics (CINTI), 18-20 Nov. 2010, Budapest, pp. 131-136.     X
Johanyák, Z.C., Szabolcsi, J.: Experiences of teaching visual programming with C# and Visual Studio 2006, Pollack Periodica, Vol. 2, Suppl., 2007, pp.97-105.      
85 A. Peculea, V. Dadarlat, I. Ignat, B. Iancu, L. Cobarzan: On developing a QoS framework with self-adaptive bandwidth reconfiguration, Pollack Periodica, 2009, Vol. 4, No. 1, pp. 121–129.     X
Johanyák, Z.C., Tikk, D., Kovács, S. and Wong, K. K.: Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox, Proc. of the IEEE World Congress on Computational Intelligence (WCCI'06), 15th Int. Conf. on Fuzzy Systems (FUZZ-IEEE'06), July 16--21, 2006, Vancouver, BC, Canada, pp. 1427-1433.      
86 White, E., Mazlack, L.J.: Discerning suicide notes causality using fuzzy cognitive maps, (2011) IEEE International Conference on Fuzzy Systems, art. no. 6007692, pp. 2940-2947.     X
87 Pozna, C., Kóczy, L.-T., Precup, R.-E., Ballagi, Á.: A kantian pattern of knowledge, the observation representation, (2010) SIISY 2010 - 8th IEEE International Symposium on Intelligent Systems and Informatics, art. no. 5647371, pp. 405-412.     X
88 C. Pozna, V. Prahovean, R.-E. Precup: A New Pattern of Knowledge Based on Experimenting the Causality Relation, Proceedings of 14th International Conference on Intelligent Engineering Systems INES 2010, Las Palmas of Gran Canaria, Spain, 2010, pp. 61-66, ISBN 978-1-4244-7651-0.     X
89 R.-E. Precup, H. Hellendoorn: A survey on industrial applications of fuzzy control, COMPUTERS IN INDUSTRY Volume: 62 Issue: 3 Pages: 213-226 DOI: 10.1016/j.compind.2010.10.001 Published: APR 2011 X X X
90 L. Yang, Q. Shen: Adaptive Fuzzy Interpolation, IEEE Transactions on Fuzzy Systems, Dec. 2011, Volume: 19, Issue: 6, pp. 1107-1126., ISSN: 1063-6706 X   X
91 M.-B. Radac, R.-E. Precup, E. M. Petriu, S. Preitl, C.-A. Dragos: Convergent Iterative Feedback Tuning of State Feedback-Controlled Servo Systems, In: Informatics in Control Automation and Robotics, Eds. Andrade Cetto, J., Filipe, J. and Ferrier, J.-L., Springer-Verlag, Berlin, Heidelberg, Lecture Notes in Electrical Engineering, vol. 85, 2011, pp. 99-111, ISBN 978-3-642-19729-1, e-ISBN 978-3-642-19730-7.     X
92 S. Biro, R.E. Precup and D. Todinca: Double inverted pendulum control by linear quadratic regulator and reinforcement learning, International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), 27-29 May 2010, Timisoara, Romania, pp. 159-164.     X
93 R.-E. Precup, S. Preitl, M.-B. Radac, E. M. Petriu, C.-A. Dragos: Experiment-Based Teaching in Advanced Control Engineering, IEEE Transactions on Education, vol. 54, no. 3, pp. 345-355, Aug. 2011, ISSN: 0018-9359, DOI: 10.1109/TE.2010.2058575, ISI SCI impact factor (in 2010) X   X
94 Shyi-Ming Chen and Yuan-Kai Ko: Fuzzy Interpolative Reasoning for Sparse Fuzzy Rule-Based Systems Based on alpha-Cuts and Transformations Techniques, IEEE Transactions on Fuzzy Systems, Dec. 2008, Volume: 16, Issue: 6, pp. 1626-1648., ISSN: 1063-6706 X   X
95 J. Botzheim, L. Gál and L.T. Kóczy: Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms, In: Studies in Computational Intelligence, Recent Advances in Decision Making, Springer, Berlin/Heidelberg, 2009, DOI: 10.1007/978-3-642-02187-9_3, pp. 21-43.     X
96 A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16. X    
97 Erra, U., Senatore, S.: Fuzzy shape classification exploiting geometrical and moments descriptors, (2011) IEEE International Conference on Fuzzy Systems, art. no. 6007702, pp. 733-740.     X
98 R.E. Precup, C. Borchescu, M.B. Radac, S. Preitl, C.A. Dragos, E. M. Petriu, J. K. Tar: Implementation and Signal Processing Aspects of Iterative Regression Tuning, Proceedings of the 2010 IEEE International Symposium on Industrial Electronics (ISIE 2010), Bary, Italy, 2010, IEEE Catalog Number: CFP10ISI-CDR, ISBN: 978-1-4244-6391-6, pp. 1657-1662. X   X
99 R.-E. Precup, F.-C. Enache, M.-B. Radac, E. M. Petriu, C.-A. Dragos, S. Preitl: Iterative Learning Control Application to a 3D Crane System, Proceedings of 8th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2011), Noordwijkerhout, The Netherlands, 2011, ISBN: 978-989-8425-74-4, vol. 1, pp. 117-122.     X
100 R.-E. Precup, P. A. Ianc, E. M. Petriu, C.-A. Dragos, S. Preitl, M.-B. Radac: Low-Cost Fuzzy Control Approaches to a Class of State Feedback- Controlled Servo Systems, Proceedings of 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM2011) Budapest, Hungary, 2011, pp. 1022-1027, ISBN 978-1-4577-0837-4, IEEE Catalog Number CFP11775-CDR, ISSN 2159-6247. X   X
101 S. Preitl, R.E. Precup, M.L. Tomescu, M.B. Radac, E.M. Petriu, C.A. Dragos: Model-Based Design Issues in Fuzzy Logic Control, Towards Intelligent Engineering and Information Technology, editors: I. J. Rudas, J. Fodor, J. Kacprzyk, Studies in Computational Intelligence, vol. 243, Springer-Verlag, Berlin, Heidelberg, ISBN 978-3-642-03736-8, ISSN 1860-949X (Print) 1860-9503 (Online), 2009, pp. 137 - 152 X   X
102 Dosoftei, C.-C., Mastacan, L.: Optimal positioning system with fuzzy logic controller, (2011) 15th International Conference on System Theory, Control and Computing, ICSTCC 2011, art. no. 6085727, .     X
103 Pozna, C., Precup, R.-E., Preitl, S., Troester, F., Tar, J.K.: Points of view on building an intelligent robot, (2009) Studies in Computational Intelligence, 243, pp. 263-277. X   X
104 C. Pozna, R.E. Precup, N. Minculete, C. Antonya, C.A. Dragos: Properties of Classes, Subclasses and Objects in an Abstraction Model, Proceedings of 19th International Workshop on Robotics in Alpe-Adria-Danube Region RAAD 2010, Budapest, Hungary, 2010, pp. 291-296, IEEE Catalog Number: CFP1075J-CDR, ISBN: 978-1-4244-6884-3.     X
105 C. Pozna, R.-E. Precup: Results concerning a new pattern of human knowledge, Proceedings of 2011 2nd International Conference on Cognitive Infocommunications CogInfoCom 2011, Budapest, Hungary, 2011, 18 pp., E-ISBN: 978-963-8111-78-4, Print ISBN: 978-1-4577-1806-9     X
106 C. Poznaa,N. Minculetec, R.E. Precup, L.T. Kóczy, Á. Ballagi: Signatures: Definitions,operatorsandapplicationsto fuzzy modelling, Fuzzy Sets and Systems, (2012) , doi: 10.1016/j.fss.2011.12.016   X  
107 S. Blažič: Takagi-Sugeno vs. Lyapunov-based tracking control for a wheeled mobile robot, WSEAS TRANSACTIONS on SYSTEMS and CONTROL (ISSN: 1991-8763), Issue 8, Volume 5, August 2010, pp. 667-676.     X
108 Detyniecki Marcin; Marsala Christophe; Rifqi Maria: Double-Linear Fuzzy Interpolation Method, IEEE International Conference on Fuzzy Systems (FUZZ 2011), Taipei, TAIWAN, JUN 27-30, 2011, pp. 455-462. X    

A fenti adatbázisokban nem szereplő hivatkozások

Hivatkozott cikk/Hivatkozó cikk
Johanyák E., Johanyák Zs. Cs.: Az ISO 9000 utat nyit a teljeskörű minőségirányítás felé, A Gépipari és Automatizálási Műszaki Főiskola Közleményei, XIII. Évfolyam 1996-1997., Kecskemét, 1997, ISSN 0230-6182,pp. 31-38.
1 Berecz, A., Kriskó, E.:: Az e-learning minőségbiztosítási megközelítései és alkalmazásuk a GDF ILIAS-ban, Informatika a Felsőoktatásban, Debrecen, 2008 augusztus 27-28, ISBN 978-963-473-129-0, pp. 1-13.
Johanyák Zs. Cs.: Fuzzy szabály-interpolációs módszerek és mintaadatok alapján történő automatikus rendszergenerálás, PhD disszertáció, Hatvany József Informatikai Tudományok Doktori Iskola, Miskolci Egyetem, Miskolc, 2007.
2 Gál, L. and Kóczy, L.T.: Advanced Bacterial Memetic Algorithms, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 225-243.
3 Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182.
4 Z. Krizsán, S. Kovács: Gradient-based Consequent Optimization of a FRI Rule Base, Production Systems and Information Engineering, Vol 5 (2009), pp. 177-188.
5 Drenyovszki, R.: Távolságmértékek a fuzzy szabály-interpolációban, XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 69-72.
Johanyák Zs. Cs.: Számítógéppel segített hibamód és -hatás elemzés, microCAD 94 - International Computer Science Conference, Miskolc, 1994. március 3., 60-67. old.
6 Antal M. R.: Exkluzív bútorok meghatározó formáinak elemzése a használati és esztétikai funkciók optmális arányainak kialakítása szempontjából, Doktori (PhD) értekezés, Nyugat-Magyarországi Egyetem, Faipari Mérnöki Kar, Cziráki József Faanyagtudomány és Technológiák Doktori Iskola, 2007.
Johanyák, Z. C.: Student Evaluation Based On Fuzzy Rule Interpolation, International Journal of Artificial Intelligence, Autumn 2010, Vol. 5, No. A10, ISSN 0974-0635, pp. 37-55.
7 Precup, R.-E., David, R.-C., Petriu, E. M., Radac, M.-B., Preitl, St. and Fodor, J.: Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems, Knowledge-Based Systems, pp. 1-11, DOI: 10.1016/j.knosys.2011.07.006, ISSN: 0950-7051
8 Precup, R.-E., David, R.-C., Petriu, E. M., Preitl, St. and Radac, M.-B.: Fuzzy control systems with reduced parametric sensitivity based on simulated annealing, IEEE Transactions on Industrial Electronics, vol. PP, no. 99, pp. 1-15, DOI: 10.1109/TIE.2011.2130493, ISSN: 0278-0046
Johanyák, Z. C.: Survey on Five Fuzzy Inference-Based Student Evaluation Methods, in I.J. Rudas et al. (Eds.):Studies in Computational Intelligence, 2010, Volume 313, Computational Intelligence in Engineering, Pages 219-228
9 F. Lilik and J. Botzheim: Fuzzy based prequalification methods for EoSHDSL technology, Acta Technica Jaurinensis Series Intelligentia Computatorica, 4(1):135–144, 2011.
10 R.-E. Precup, M.-L. Tomescu, E. M. Petriu, L.-E. Dragomir: Stable Fuzzy Logic Control of Generalized van der Pol Oscillator, International Journal of Artificial Intelligence, vol. 7, no. A11, pp. 36-46, Oct. 2011, ISSN 0974-0635
Johanyák, Z. C.: Fuzzy Rule Interpolation based on Subsethood Values, in Proceedings of 2010 IEEE Interenational Conference on Systems Man, and Cybernetics (SMC 2010), 10-13 October 2010, ISBN 978-1-424-6587-3, pp. 2387-2393.
11 S. Preitl, R.-E. Precup: Linear and Fuzzy Control Extensions of the Symmetrical Optimum Method, Proceedings of Special International Conference on Complex Systems: Synergy of Control, Communications and Computing (COSY 2011), Ohrid, Republic of Macedonia, 2011, pp. 59-68, ISBN 978-9989-2175-8-6.
Johanyák, Z. C., Ádámné, A.M.: Fuzzy Modeling of the Relation between Components of Thermoplastic Composites and their Mechanical Properties, Proceedings of the 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009), May 28-29, 2009, Timisoara, Romania, pp. 481-486.
12 F. Lilik and J. Botzheim: Fuzzy based prequalification methods for EoSHDSL technology, Acta Technica Jaurinensis Series Intelligentia Computatorica, 4(1):135–144, 2011.
Johanyák, Z. C., Kovács, S.: Sparse Fuzzy System Generation by Rule Base Extension, 11th IEEE International Conference of Intelligent Engineering Systems (IEEE INES 2007), June 29 - July 1, 2007, Budapest, ISBN 1-4244-1148-3, pp. 99-104
13 M.L.Tomescu, R.E. Precup, S. Preitl, S. Blazic: Elements of Intelligence in Control of a Class of Nonlinear Time-Varying Systems, Proceedings of the International Symposium - Research and Education in Innovation Era, Section Mathematics and Computer Science, 2nd Edition, Ed. Universităţii „Aurel Vlaicu” din Arad, Arad (2008), ISSN 2065-2569., pp. 221-233.
14 C.A. Dragoş, S. Preitl and R.E. Precup: Low-cost Takagi-Sugeno Fuzzy Controller for an Electromagnetic Actuator, Scientific Bulletin of “Politehnica” University of Timisoara, Romania, Transactions on Automatic Control and Computer Science, Vol. 54 (68), Fasc. 2, 2009, ISSN 1224-600X, pp. 87-92
15 R.-E. Precup, E. M. Petriu, C.-A. Dragos, R.-C. David: Stability Analysis Results Concerning the Fuzzy Control of a Class of Nonlinear Time-Varying Systems, Theory and Applications of Mathematics & Computer Science, vol. 1, no. 1, 2011, pp. 2-10, ISSN 2067-2764
Johanyák, Z.C.: Vague Environment Based Set Interpolation, A GAMF Közleményei, Kecskemét, XXI. évfolyam (2006-2007), ISSN 0230-6182, pp. 33-44
16 Vincze, D., Kovács, S.: Using fuzzy rule interpolation based automata for controlling navigation and collision avoidance behaviour of a robot, IEEE 6th International Conference on Computational Cybernetics, November 27-29, 2008, Stara Lesná, Slovakia, pp. 79-84.
Johanyák, Z.C. and Alvarez Gil, R. P.: Generalization of the single rule reasoning method SURE-LS for the case of arbitrary polygonal shaped fuzzy sets, Annals of the Faculty of Engineering Hunedoara, ISSN 1584-2665, Tome VI (2008), Fascicule 2, pp. 161-170.
17 Morioka, K., Kovács, S., Korondi, P., Lee, J.-H., Hashimoto, H.: Adaptive Camera Selection Based On Fuzzy Automaton For Object Tracking In A Multicamera System, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182.
Johanyák, Z.C. and Kovács, S.: Fuzzy set approximation using polar co-ordinates and linguistic term shifting, 4rd Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence (SAMI 2006), Herl'any, Slovakia, 2006, pp. 219-227.
18 Krizsán, Z.: Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Johanyák, Z.C. and Szabó, A.: Tool life modelling using RBE-DSS method and LESFRI inference mechanism, A GAMF Közleményei, Kecskemét, XXII. (2008), ISSN 0230-6182, pp. 17-28.
19 Kovács, S.: Fuzzy Rule Interpolation from a Practical Point of View, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 83-101.
20 Z. Krizsán, S. Kovács: Gradient-based Consequent Optimization of a FRI Rule Base, Production Systems and Information Engineering, Vol 5 (2009), pp. 177-188.
21 Vincze, D., Kovács, S.: Using fuzzy rule interpolation based automata for controlling navigation and collision avoidance behaviour of a robot, IEEE 6th International Conference on Computational Cybernetics, November 27-29, 2008, Stara Lesná, Slovakia, pp. 79-84.
Johanyák, Z.C., Dr. Kovács Sz: A fuzzy tagsági függvény megválasztásáról, A GAMF Közleményei, Kecskemét, XIX. évfolyam (2004), ISSN 0230-6182, pp. 73-84.
22 G. Molnárka: Fuzzy Expert System as a Decision Support Tool in the Visual Examination Process in Building Diagnostics, CIB 2010 World Congress, May, 2010, Manchester, UK, pp. 1-15.
Johanyák, Z.C., Kovács, S.: Neuro-fuzzy módszerek alkalmazása a kísérletmódszertanban, A GAMF Közleményei, Kecskemét, XX. évfolyam (2005), ISSN 0230-6182, pp. 37-48.
23 Varga Tamás: A fuzzy logika alklmazási lehetőségei a minőségtetrvezésben, Debreceni Műszaki Közlemények, 2010/1, HU ISSN 2060 - 6869, pp. 43-51
Johanyák, Z.C., Kovács, S.: Distance based similarity measures of fuzzy sets, SAMI 2005, 3rd Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence, Herl'any, Slovakia, January 21-22 2005, ISBN 963 7154 35 3, pp. 265-276.
24 Vít Nováček: A Non-traditional Inference Paradigm for Learned Ontologies, Proceedings of the KWEPSY 2007 Knowledge Web PhD Symposium,Innsbruck, Austria, June 6, 2007, CEUR Workshop Proceedings, ISSN 1613-0073, pp- 57-62.
25 Freitag, S., Graf, W., Kaliske, M.: Identification and prediction of time-dependent structural behavior with recurrent neural networks for uncertain data, 4th International Workshop on Reliable Engineering Computing (REC 2010) Ed. Michael Beer, Rafi L. Muhanna and Robert L. Mullen, ISBN: 978-981-08-5118-7, pp. 577-596.
26 K. S. Ray, M. Mondal: Similarity-based fuzzy reasoning by DNA computing, International Journal of Bio-Inspired Computation, Vol. 3 Issue 2, April 2011, pp. 112--122, doi>10.1504/IJBIC.2011.039910, ISSN 1758-0366
27 Drenyovszki, R.: Távolságmértékek a fuzzy szabály-interpolációban, XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 69-72.
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-513
28 Gál, L. and Kóczy, L.T.: Advanced Bacterial Memetic Algorithms, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 225-243.
Johanyák, Z.C., Kovács, S.: Sparse Fuzzy System Generation by Rule Base Extension, 11th IEEE International Conference of Intelligent Engineering Systems (IEEE INES 2007), June 29 - July 1, 2007, Budapest, ISBN 1-4244-1148-3, pp. 99-104
29 Gál, L. and Kóczy, L.T.: Advanced Bacterial Memetic Algorithms, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 225-243.
30 Krizsán, Z.: Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-525
31 S. Preitl, R.-E. Precup, Z. Preitl: Aspects concerning the tuning of 2-DOF fuzzy controllers, Facta Universitatis, Series: Automatic Control and Robotics, vol. 9, no, 1, 2010, pp. 1 - 18 (University of Nis, Serbia, ISSN 1820-6417)
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-523
32 S. Preitl, R.-E. Precup, Z. Preitl: Aspects Concerning the Tuning of 2-DOF Fuzzy Controllers, Proceedings of Xth Triennial International SAUM Conference on Systems, Automatic Control and Measurements SAUM 2010, Nis, Serbia, ISBN 978-86-6125-020-0, pp. 210-219.
Johanyák, Z.C., Kovács, S.: A brief survey and comparison on various interpolation based fuzzy reasoning methods, Acta Polytechnica Hungarica, Vol. 3, No. 1, 2006, ISSN 1785-8860, pp. 91-105.
33 Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182.
34 C.-A. Dragos, R.-E. Precup, S. Preitl, E. M. Petriu., M.-B. Radac: Control Solutions, Simulation and Experimental Results for a Magnetic Levitation Laboratory System, Proceedings of 7th EUROSIM Congress on Modelling and Simulation (EUROSIM 2010), Prague, Czech Republic, 2010, Editors: M. Snorek, Z. Buk, M. Cepek, J. Drchal, ISBN 978-80-01-04589-3, Vol. 2: Full Papers (CD), paper index 155, 8 pp.
35 M.-B. Radac, R.-E. Precup, E. M. Petriu, S. Preitl, C.-A. Dragos: Convergent Iterative Feedback Tuning of State Feedback-Controlled Servo Systems, In: Informatics in Control Automation and Robotics, Eds. Andrade Cetto, J., Filipe, J. and Ferrier, J.-L., Springer-Verlag, Berlin, Heidelberg, Lecture Notes in Electrical Engineering, vol. 85, 2011, pp. 99-111, ISBN 978-3-642-19729-1, e-ISBN 978-3-642-19730-7.
36 Precup, R.-E., David, R.-C., Petriu, E. M., Radac, M.-B., Preitl, St. and Fodor, J.: Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems, Knowledge-Based Systems, pp. 1-11, DOI: 10.1016/j.knosys.2011.07.006, ISSN: 0950-7051
37 C. Pozna, R.-E. Precup, S. Preitl, E. M. Petriu, J. K. Tar: Structure for Behaviourist Representation of Knowledge, Proceedings of 10th International Symposium of Hungarian Researchers in Computational Intelligence and Informatics CINTI 2009, Budapest, Hungary, 2009, pp. 55-68, ISBN 978-963-7154-97-6.
38 Vaščák, J.: Using Neural Gas Networks in Traffic Navigation, Acta Technica Jaurensis, Series Intelligentia Computatorica, vol. 2, no. 2, Dec. 2009, ISSN 1789-6932, Szechenyi Istvan University, Faculty of Electrical Engineering, Gyor, Hungary, pp. 203-215.
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-511
39 Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182.
40 Drenyovszki, R.: Távolságmértékek a fuzzy szabály-interpolációban, XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 69-72.
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation by the Least Squares Method, 7th International Symposium of Hungarian Researchers on Computational Intelligence (HUCI 2006), November 24-25, 2006 Budapest, ISBN 963 7154 54 X, pp. 495-506.
41 Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182.
42 Krizsán, Z.: Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Johanyák, Z.C., Kovács, S.: Survey on three single rule reasoning methods, A GAMF Közleményei, Kecskemét, XXI. évfolyam (2006-2007), ISSN 0230-6182, pp. 75-86.
43 Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182.
Johanyák, Z.C., Kovács, S.: Vague Environment-based Two-step Fuzzy Rule Interpolation Method, 5th Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence and Informatics (SAMI 2007), January 25-26, 2007 Poprad, Slovakia, ISBN 978 963 7154 56 0, pp. 189-200.
44 Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182.
45 Krizsán, Z.: Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-518
46 R.-E. Precup, R.-C. David, S. Preitl, E. M. Petriu: Design aspects of optimal PI controllers with reduced sensitivity for a class of servo systems using PSO algorithms, Facta Universitatis, Series Automatic Control and Robotics, University of Nis (Serbia), ISSN 1820-6417, vol. 8, no. 1, 2009, pp. 1 - 12.
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-520
47 C. Pozna, L.-T. Koczy, R.-E. Precup, A. Ballagi, A Kantian: Pattern of Knowledge, the Observation Representation, Proceedings of 8th IEEE International Symposium on Intelligent Systems and Informatics SISY 2010, Subotica, Serbia, 2010, pp. 405-412, ISBN: 978-1-4244-7395-3, IEEE Catalog Number: CFP1084C-CDR.
Johanyák, Z.C., Kovács, S.: The effect of different fuzzy partition parameterization strategies in gradient descent parameter identification, 4th International Symposium on Applied Computational Intelligence and Informatics (SACI 2007), May 17-18, 2007 Timisoara, Romania, pp. 141-146.
48 Precup, R. E.,Tomescu, M. L. and Preitl, St. : Rule base modification of Takagi-Sugeno fuzzy logic controllers to guarantee system stability, Bulletins for applied & Computer mathematics (BAM), CXII/2008 Nr. 2363, ISSN 0133-3526, pp. 113-120.
49 A.S. Paul , R.E. Precup , J. Fodor and M.B. Rădac: Software Issues in Experimental Setups for Audio Signal Processing, Scientific Bulletin of “Politehnica” University of Timisoara, Romania, Transactions on Automatic Control and Computer Science, Vol. 54 (68), Fasc. 1, 2009, ISSN 1224-600X, pp. 31-38.
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-522
50 R.-E. Precup, C.-A. Dragos, S. Preitl, E. M. Petriu, M.-B. Radac, A. S. Paul: Stable fuzzy control of an electromagnetic actuated clutch, Annals of the University of Craiova, Series: Automation, Computers, Electronics and Mechatronics, vol. 7 (34), no. 1, Oct. 2010, pp. 53-60, ISSN 1841-0626.
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-529
51 R.-E. Precup, M.-L. Tomescu, E. M. Petriu, L.-E. Dragomir: Stable Fuzzy Logic Control of Generalized van der Pol Oscillator, International Journal of Artificial Intelligence, vol. 7, no. A11, pp. 36-46, Oct. 2011, ISSN 0974-0635
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Ed: Bernd Reusch, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-521
52 R.-E. Precup, C.-A. Dragos, S. Preitl, M.-B. Radac, E. M. Petriu: Tensor Product Models for Automotive Applications, Proceedings of 14th International Conference on System Theory and Control, Craiova, Romania, 2010, Editura Universitaria Craiova Publishers, Craiova, Romania, ISSN 2068-0465, pp. 405-410.
Johanyák, Z.C., Kovács, S.: A brief survey on fuzzy set interpolation methods, Doktoranduszok Fóruma, Miskolci Egyetem, 9 November 2006, pp. 72-77.
53 Krizsán, Z.: Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Johanyák, Z.C., Kovács, S.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-512
54 Krizsán, Z.: Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Johanyák, Z.C., Kovács, S.: Fuzzy Set Approximation by Weighted Least Squares regression, Annals of the Faculty of Engineering Hunedoara 2006, Tome IV, Fascicule 1, ISSN 1584-2665, pp. 27-34.
55 Krizsán, Z.: Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Johanyák, Z.C., Parthiban, R, and Sekaran, G.: Fuzzy Modeling for an Anaerobic Tapered Fluidized Bed Reactor, SCIENTIFIC BULLETIN of “Politehnica” University of Timisoara, ROMANIA, Transactions on AUTOMATIC CONTROL and COMPUTER SCIENCE, ISSN 1224-600X, Vol: 52(66) No: 2 / 2007, pp. 67-72.
56 Vincze, D., Kovács, S.: Applying Fuzzy Rule Interpolation for the Task of Controlling Guidance and Obstacle Avoidance Behaviour of a Robot, Proceedings of the 9th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, Budapest, Hungary, November 6-8, 2008, ISBN 978-963-7154-82-9, pp. 229-241.
57 D. Vincze, S. Kovács: Behaviour Based Control with Fuzzy Automaton in Vehicle Navigation, Production Systems and Information Engineering, Vol 5 (2009), pp. 151-166.
58 Precup, R. E., Preitl, S., Ursache, I. B., Clep, P. A. and Spanu, F.: Experiments in Linear and Sliding Mode Control of First- and Second-order Lag Plus Dead Time Processes, SCIENTIFIC BULLETIN of “Politehnica” University of Timisoara, ROMANIA, Transactions on AUTOMATIC CONTROL and COMPUTER SCIENCE, ISSN 1224-600X, Vol: 52(66) No: 3 / 2007, pp. 115-126.
59 Z. Krizsán, S. Kovács: Gradient-based Consequent Optimization of a FRI Rule Base, Production Systems and Information Engineering, Vol 5 (2009), pp. 177-188.
60 Vincze, D., Kovács, S.: Using fuzzy rule interpolation based automata for controlling navigation and collision avoidance behaviour of a robot, IEEE 6th International Conference on Computational Cybernetics, November 27-29, 2008, Stara Lesná, Slovakia, pp. 79-84.
Johanyák, Z.C., Szabolcsi, J.: Experiences of teaching visual programming with C# and Visual Studio 2005, Pollack Periodica, Vol. 2, Suppl., 2007, pp.97-105.
61 Illés, A.: Microsoft Visual Programming Language a szoftverfejlesztés oktatásban, XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 131-134.
Johanyák, Z.C., Tikk, D., Kovács, S. and Wong, K. K.: Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox, Proc. of the IEEE World Congress on Computational Intelligence (WCCI'06), 15th Int. Conf. on Fuzzy Systems (FUZZ-IEEE'06), July 16--21, 2006, Vancouver, BC, Canada, pp. 1427-1433.
62 Gál, L. and Kóczy, L.T.: Advanced Bacterial Memetic Algorithms, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 225-243.
63 S. Preitl, R.-E. Precup, Z. Preitl: Aspects concerning the tuning of 2-DOF fuzzy controllers, Facta Universitatis, Series: Automatic Control and Robotics, vol. 9, no, 1, 2010, pp. 1 - 18 (University of Nis, Serbia, ISSN 1820-6417)
64 S. Preitl, R.-E. Precup, Z. Preitl: Aspects Concerning the Tuning of 2-DOF Fuzzy Controllers, Proceedings of Xth Triennial International SAUM Conference on Systems, Automatic Control and Measurements SAUM 2010, Nis, Serbia, ISBN 978-86-6125-020-0, pp. 210-219.
65 C.-A. Dragos, R.-E. Precup, S. Preitl, E. M. Petriu., M.-B. Radac: Control Solutions, Simulation and Experimental Results for a Magnetic Levitation Laboratory System, Proceedings of 7th EUROSIM Congress on Modelling and Simulation (EUROSIM 2010), Prague, Czech Republic, 2010, Editors: M. Snorek, Z. Buk, M. Cepek, J. Drchal, ISBN 978-80-01-04589-3, Vol. 2: Full Papers (CD), paper index 155, 8 pp.
66 R.-E. Precup, R.-C. David, S. Preitl, E. M. Petriu: Design aspects of optimal PI controllers with reduced sensitivity for a class of servo systems using PSO algorithms, Facta Universitatis, Series Automatic Control and Robotics, University of Nis (Serbia), ISSN 1820-6417, vol. 8, no. 1, 2009, pp. 1 - 12.
67 R.E. Precup, M.L. Tomescu, S. Preitl, E.M. Petriu: Fuzzy Logic-based Stabilization of Nonlinear Time-Varying Systems, International Journal of Artificial Intelligence, Autumn 2009, Vol. 3, No. A09, ISSN 0974-0635, pp. 24-36.
68 C.-A. Dragos, S. Preitl, R.-E. Precup, C.-S. Nes, E. M. Petriu, G. Tirtea: One- and Two-Degree-of-Freedom Fuzzy Control of an Eletromgnetic Actuated Clutch, Proceedings of 14th International Conference on System Theory and Control, Craiova, Romania, 2010, Editura Universitaria Craiova Publishers, Craiova, Romania, ISSN 2068-0465, pp. 190-195.
69 C. Pozna, L.-T. Koczy, R.-E. Precup, A. Ballagi, A Kantian: Pattern of Knowledge, the Observation Representation, Proceedings of 8th IEEE International Symposium on Intelligent Systems and Informatics SISY 2010, Subotica, Serbia, 2010, pp. 405-412, ISBN: 978-1-4244-7395-3, IEEE Catalog Number: CFP1084C-CDR.
70 R.-E. Precup, E. M. Petriu, C.-A. Dragos, R.-C. David: Stability Analysis Results Concerning the Fuzzy Control of a Class of Nonlinear Time-Varying Systems, Theory and Applications of Mathematics & Computer Science, vol. 1, no. 1, 2011, pp. 2-10, ISSN 2067-2764
71 R.-E. Precup, M.-L. Tomescu, S. Preitl, E. M. Petriu, J. Fodor, A. S. Paul: Stable design of Takagi-Sugeno fuzzy controllers for a laboratory three-tank system, International Journal of Nuclear Knowledge Management, vol. 5, no. 2, 2011, pp. 126-147, ISSN (Online): 1479-5418, ISSN (Print): 1479-540X
72 C.A. Dragos, S. Preitl, R.E. Precup, R.G. Bulzan, C. Pozna, J.K. Tar: Takagi-Sugeno Fuzzy Controller for a Magnetic Levitation System Laboratory Equipment, International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), 27-29 May 2010, Timisoara, Romania, pp. 55-60.
Johanyák, Z.C., Tóth, Gy. F.: Vizuális módszerek oktatásának hatása a hallgatók programozási hibáira, Matematika-, fizika- és számítástechnika oktatók XXXI. konferenciája, Dunaújváros, 2007. augusztus 23-25., pp. 126-131.
73 Illés, A.: Microsoft Visual Programming Language a szoftverfejlesztés oktatásban, XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 131-134.
Tóth, G.F., Johanyák, Z.C.: Teaching software engineering - Experiences and new Approaches, XIX Didmattech 2006, September 6-7, Komarno, Slovakia , ISBN 978-80-89234-23-3 , 261-265.
74 Illés, A.: Microsoft Visual Programming Language a szoftverfejlesztés oktatásban, XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 131-134.
1 Cited paper: Johanyák, Zs. Cs., Kovács, Sz.: Vague Environment-based Two-step Fuzzy Rule Interpolation Method, 5th Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence and Informatics (SAMI 2007), January 25-26, 2007 Poprad, Slovakia, ISBN 978 963 7154 56 0, pp. 189-200. [pdf  
  1 Cited by: Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182. [pdf]
Context:  
This paper tries to fill this gap by analyzing the computational complexity of three single rule reasoning methods (SURE-p [3], SURE-LS [4] and REVE [6]) that were developed for the second step of the generalized methodology of fuzzy rule interpolation (GM) [1]. ...
...The method REVE proposed by Johanyák and Kovács [6] is based on the concept of Vague Environment (VE) originally introduced by Klawonn [8].
 
 
  2 Cited by: Krizsán, Z.:Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Context: Relevant members of the two-step FRI methods are the techniques suggested by Baranyi, Kóczy and Gedeon [1], the LESFRI [11], FRIPOC [3] and VEIN [12] developed by Johanyák and Kovács as well as the IGRV proposed by Huang and Shen [2].
 
2 Cited paper: Johanyák, Zs. Cs., Kovács, Sz.: Survey on three single rule reasoning methods, A GAMF Közleményei, Kecskemét, XXI. évfolyam (2006-2007), ISSN 0230-6182, pp. 75-86.
  1 Cited by: Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182. [pdf]
Context:  Johanyák and Kovács also published and applied in [5] a specialized condition set aiming the characterization of single rule reasoning methods.
 
3 Cited paper: Johanyák, Zs. Cs., Parthiban, R, and Sekaran, G.: Fuzzy Modeling for an Anaerobic Tapered Fluidized Bed Reactor, SCIENTIFIC BULLETIN of “Politehnica” University of Timisoara, ROMANIA, Transactions on AUTOMATIC CONTROL and COMPUTER SCIENCE, ISSN 1224-600X, Vol: 52(66) No: 2 / 2007, pp. 67-72. [link]
  1 Cited by: Precup, R. E., Preitl, S., Ursache, I. B., Clep, P. A. and Spanu, F.: Experiments in Linear and Sliding Mode Control of First- and Second-order Lag Plus Dead Time Processes, SCIENTIFIC BULLETIN of “Politehnica” University of Timisoara, ROMANIA, Transactions on AUTOMATIC CONTROL and COMPUTER SCIENCE, ISSN 1224-600X, Vol: 52(66) No: 3 / 2007, pp. 115-126.
Context: The applications need serious analyses depending on the control system involved. Tensor product models, linear matrix inequalities, iterative methods represent authors’ intentions [19-21]. On the other hand, the area of applications will be extended [22-24].
 
  2 Cited by: Vincze, D., Kovács, Sz.: Applying Fuzzy Rule Interpolation for the Task of Controlling Guidance and Obstacle Avoidance Behaviour of a Robot, Proceedings of the 9th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, Budapest, Hungary, November 6-8, 2008, ISBN 978-963-7154-82-9, pp. 229-241.
Context: Recently FRI methods have been successfully adapted in several practical application areas like fuzzy modelling of an anaerobic tapered fluidized bed reactor (Johanyák et al. [14]).
 
  3 Cited by: Vincze, D. and Kovács, S.: Using fuzzy rule interpolation based automata for controlling navigation and collision avoidance behaviour of a robot, IEEE 6th International Conference on Computational Cybernetics, November 27-29, 2008, Stara Lesná, Slovakia, pp. 79-84.
Context: In [14] Johanyák et al. introduces an automatic way for direct sparse fuzzy rule base generation based on given input-output data.
 
  4 Cited by: Kovács, Sz.: Fuzzy Rule Interpolation from a Practical Point of View, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 83-101. [link]
Context: FRI methods have been also successfully applied in several other areas like fuzzy modeling of an anaerobic tapered fluidized bed reactor (Johanyák et al. [9]) or tool life modeling (Johanyák and Szabó [10]).
 
  5 Cited by: Z. Krizsán, S. Kovács: Gradient-based Consequent Optimization of a FRI Rule Base, Production Systems and Information Engineering, Vol 5 (2009), pp. 177-188.
Context: For example Johanyák, Parthiban and Sekaran [2] developed fuzzy models for an anaerobic tapered fluidized bed reactor, Johanyák and Szabó [3] used a FRI based fuzzy model for tool life prediction depending on cutting parameters in thhe case of machining operations.
 
  6 Cited by: D. Vincze, S. Kovács: Behaviour Based Control with Fuzzy Automaton in Vehicle Navigation, Production Systems and Information Engineering, Vol 5 (2009), pp. 151-166.
Context: In [6] Johanyák et al introduce an automatic way for direct sparse fuzzy rule base generation based on given input-output data.
 
  7 Cited by: D. Vincze, S. Kovács: Performance issues of the implemented FRI 'FIVE', 11th International Symposium on Computational Intelligence and Informatics (CINTI), 18-20 Nov. 2010, Budapest, pp. 131-136. [IEEE link]
Context: In [4] Johanyák et al introduce an automatic way for direct sparse fuzzy rule base generation based on given input-output data.
 
4 Cited paper: Johanyák, Zs. Cs., Kovács Sz.: A brief survey and comparison on various interpolation based fuzzy reasoning methods, Acta Polytechnica Hungarica, Vol. 3, No. 1, 2006, ISSN 1785-8860, pp. 91-105
  1 Cited by: Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182. [pdf]]
Context: Johanyák and Kovács [2] survey and evaluate a wider range of FRI methods based on a self defined application oriented evaluation criteria set.
 
  2 Cited by: Lior Shamir :A proposed stereo matching algorithm for noisy sets of color imagesComputers & Geosciences ISSN:0098-3004, Volume 33, Issue 8, August 2007, Pages 1052-1063 [link]
Context: All membership functions are in the form of triangular and trapezoidal functions (Zadeh, 1965), which are efficient in terms of computational complexity (Johanyak and Kovacs, 2006).
 
ISI Web of Science link
  3 Cited by: C. Pozna, R.-E. Precup, S. Preitl, E. M. Petriu, J. K. Tar: Structure for Behaviourist Representation of Knowledge, Proceedings of 10th International Symposium of Hungarian Researchers in Computational Intelligence and Informatics CINTI 2009, Budapest, Hungary, 2009, pp. 55-68, ISBN 978-963-7154-97-6.
Context: Another future research direction concerns the design of control structures for the autonomous car and similar autonomous robots. Use will be made of low cost models and controllers that should ensure very good control system performance indices [17-27].
 
  4 Cited by: Vaščák, J.: Using Neural Gas Networks in Traffic Navigation, Acta Technica Jaurensis, Series Intelligentia Computatorica, vol. 2, no. 2, Dec. 2009, ISSN 1789-6932, Szechenyi Istvan University, Faculty of Electrical Engineering, Gyor, Hungary, pp. 203-215.
Context: Their ability of accurate description for a given pattern was compared to other kinds of modeling techniques e.g. in [7,12,21]
 
  5 Cited by: J. Vascak, Approaches in Adaptation of Fuzzy Cognitive Maps for Navigation Purposes, Proceedings of 8th IEEE International Symposium on Applied Machine Intelligence and Informatics SAMI 2010, Herl'any, Slovakia, ISBN 978-1-4244-6423-4, pp. 31-36.
Context: Therefore it could be very useful also to focus interest on various interpolation and nonlinear methods already used in conventional rule-baes fuzzy systems [5,10]
 
ISI Web of Science link
  6 Cited by: C.A. Dragos, S. Preitl, R.E. Precup, R.G. Bulzan, C. Pozna, J.K. Tar: Takagi-Sugeno Fuzzy Controller for a Magnetic Levitation System Laboratory Equipment, International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), 27-29 May 2010, Timisoara, Romania, pp. 55-60. [link]
Context: The new control solutions given in this paper are designed by the development of our previous design methods [6]–[8] and of the other popular fuzzy control and logic approaches [9]–[17].
  7 Cited by: A.S. Paul, R.E. Precup, C. Pozna, R.C. David: nDSP: A Platform for Audiophile Software Audio Processing, International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), 27-29 May 2010, Timisoara, Romania, pp. 431-436. [link]
Context: Another research direction will deal with the adaptive audio filters systematically designed and implemented [19]–[31] using fuzzy control algorithms.
  8 Cited by: C.-A. Dragos, R.-E. Precup, S. Preitl, E. M. Petriu., M.-B. Radac, Control Solutions, Simulation and Experimental Results for a Magnetic Levitation Laboratory System, Proceedings of 7th EUROSIM Congress on Modelling and Simulation (EUROSIM 2010), Prague, Czech Republic, 2010, Editors: M. Snorek, Z. Buk, M. Cepek, J. Drchal, ISBN 978-80-01-04589-3, Vol. 2: Full Papers (CD), paper index 155, 8 pp.
Context: Such solutions can be implemented easily in other control system structures and models [13,14,15,16,17,18,19,20,21,22, 23,24].
  9 Cited by: J. Vascak and L. Madarasz, "Adaptation of fuzzy cognitive maps - a comparison study", Acta Polytechnica Hungarica, vol. 7, no. 3, pp. 109-122, 2010.
Context: Therefore it could also be very useful to focus interest on various interpolation and nonlinear methods already used in conventional rule-based fuzzy systems [5, 11].
5 Cited paper: Johanyák, Zs. Cs., Dr. Kovács Sz.: Distance based similarity measures of fuzzy sets, SAMI 2005, 3rd Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence, Herl'any, Slovakia, January 21-22 2005, ISBN 963 7154 35 3, pp. 265-276. [pdf]
  1 Cited by: Vít Nováček: A Non-traditional Inference Paradigm for Learned Ontologies, Proceedings of the KWEPSY 2007 Knowledge Web PhD Symposium,Innsbruck, Austria, June 6, 2007, CEUR Workshop Proceedings, ISSN 1613-0073, pp- 57-62. [link]
Context: Very valuable concept in this respect is the notion of analogical reasoning [12] and its fuzzy extension [2]. The latter can be further developed in the scope of our work with different notions of fuzzy similarity [22, 11].
 
  2 Cited by: Drenyovszki, R.: Távolságmértékek a fuzzy szabály-interpolációban, XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 69-72. [pdf]
Context
Ilyenkor általában fuzzy szabály-interpoláción alapuló eljárásokat alkalmaznak, ahol a fuzzy halmazok hasonlóságának és sorrendjének vizsgálata is szükséges. A hasonlóságot és sorrendet a legtöbb esetben a halmazok távolságának felhasználásával tudjuk meghatározni [2]. ...
...Egy távolságfüggvényt az alábbi négy feltétel teljesülése esetén tekinthetünk metrikának [2]: ...
 
  3 Cited by: Graf, W., Freitag, S., Kaliske, M., Sickert, J.U.: Recurrent Neural Networks for Uncertain Time-Dependent Structural Behavior, COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Vol. 25, Issue 5, Special Issue: Sp. Iss. SI, July 2010, ISSN: 1093-9687 , pp. 322-333.
Context: In general, different error measures are possible (see, e.g., Johanyák and Kovács, 2005)
ISI Web of Science link
  4 Cited by: Freitag, S., Graf, W., Kaliske, M., : Identification and prediction of time-dependent structural behavior with recurrent neural networks for uncertain data, 4th International Workshop on Reliable Engineering Computing (REC 2010) Ed. Michael Beer, Rafi L. Muhanna and Robert L. Mullen, ISBN: 978-981-08-5118-7, pp. 577-596. [pdf]
Context: In general, different distance measures are possible, see e.g. (Beer, 2007) and (Johanyák and Kovács, 2005)
 
  5 Cited by: H.H. Huang, Y.H. Kuo: Cross-Lingual Document Representation and Semantic Similarity Measure: A Fuzzy Set and Rough Set Based Approach, IEEE Transactions on Fuzzy Systems, ISSN: 1063-6706, Vol. 18, Issue:6, pp. 1098 - 1111
Context:
IEEE Xplore
  6 Cited by: C.-A. Dragos, S. Preitl, R.-E. Precup, C. S. Nes, E. M. Petriu, Model Predictive Control Solutions for Vehicular Power Train Systems, Bulletin of the Polytehnic Institute of Iasi, Automatic Control and Computer Science Section, vol. 56 (60) no. 4, 2010, pp. 27-40, "Gheorghe Asachi" Technical University of Iasi, Romania, ISSN 1220-2169.
Context: The future research will be dedicated to the improvement of the control systems behaviours at several operating points by inserting additional controller functionalities [1], [4], [8] ...
 
6 Cited paper: Johanyák, Zs. Cs., Kovács Sz.: Fuzzy Rule Interpolation Based on Polar Cuts, Computational Intelligence, Theory and Applications, Springer Berlin Heidelberg, 2006, pp. 499-511 [Springer link]
  1 Cited by: Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182. [pdf]
Context:
This paper tries to fill this gap by analyzing the computational complexity of three single rule reasoning methods (SURE-p [3], SURE-LS [4] and REVE [6]) that were developed for the second step of the generalized methodology of fuzzy rule interpolation (GM) [1]. ...
...The Single rUle REasoning based on polar cuts (SURE-p) was introduced by Johanyák and Kovács in [3] as a complement method of the set interpolation technique FEAT-p [3]. ...
 

  2 Cited by: Drenyovszki, R.: Távolságmértékek a fuzzy szabály-interpolációban, XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 69-72. [pdf]
Context: Ritka szabálybázisra épülő rendszerben a hagyományos kompozíciós következtetési módszerek (Zadeh [8], Mamdani [6], stb.) segítségével nem tudunk helyes eredményeket előállítani minden lehetséges bemenő érték esetén [1][3][4][5]. Ilyenkor általában fuzzy szabály-interpoláción alapuló eljárásokat alkalmaznak, ahol a fuzzy halmazok hasonlóságának és sorrendjének vizsgálata is szükséges.
 
  3 Cited by: Krizsán, Z.: Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Context: Relevant members of the two-step FRI methods are the techniques suggested by Baranyi, Kóczy and Gedeon [1], the LESFRI [11], FRIPOC [3] and VEIN [12] developed by Johanyák and Kovács as well as the IGRV proposed by Huang and Shen [2].
 
  4 Cited by: Gál, L. and Kóczy, L.T.: Advanced Bacterial Memetic Algorithms, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 225-243. [link]
Context: Most of the methods mentioned above are also able to identify fuzzy models with low complexity by generating sparse rule bases. These systems use fuzzy rule interpolation (FRI) based reasoning (e.g. [15], [16], and [12]).
  5 Cited by: J. Botzheim, L. Gál and L.T. Kóczy: Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms, In: Studies in Computational Intelligence, Recent Advances in Decision Making, Springer, Berlin/Heidelberg, 2009, DOI: 10.1007/978-3-642-02187-9_3, pp. 21-43.
Context: Most of the methods mentioned above are also able to identify fuzzy models with low complexity by generating sparse rule bases. These systems use fuzzy rule interpolation (FRI) based reasoning (e.g. [14], [15], and [11]).
  6 Cited by: A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16.
Context: The technique FRIPOC [13] used in this paper belongs to the second group.
Relevant members of this family are among others ... the FRIPOC introduced by Johanyák and Kovács [13].
The FRIPOC method developed by Johanyák and Kovács [13] follows GM.
The FEAT-p set interpolation based on linguistic term shifting and polar cuts, proposed by Johanyák and Kovács in [13] solves the task of the set interpolation by the help of linguistic values and polar cuts.
The SURE-p [13] proposed by Johanyák and Kovács solves the task of the rule modification by the help of polar cuts and weighted mean calculation.
  7 Cited by: R.E. Precup, I. Mosincat, M.B. Radac, S. Preitl, S. Kilyeni, E.M. Petriu, C.A. Dragos: Experiments in Iterative Feedback Tuning for Level Control of Three-Tank System, Proceedings of 15th IEEE Mediterranean Electromechanical Conference MELECON 2010, Valletta, Malta, 2010, pp. 564-569, ISBN 978-1-4244-5794-6, IEEE Catalog number: CFP10MEL-CDR.[link]
Context: Future research will be focused on the convergence analysis of IFT algorithms. All theoretical results will be tested in the control of complex processes that involve also fuzzy controllers [27]–[34].
  8 Cited by: C. Pozna, V. Prahovean, R.-E. Precup: A New Pattern of Knowledge Based on Experimenting the Causality Relation, Proceedings of 14th International Conference on Intelligent Engineering Systems INES 2010, Las Palmas of Gran Canaria, Spain, 2010, pp. 61-66, ISBN 978-1-4244-7651-0. [link]
Context: Use will be made of different AI tools including fuzzy logic and neural networks [19] – [28].
  9 Cited by: A.S. Paul, R.E. Precup, C. Pozna, R.C. David: nDSP: A Platform for Audiophile Software Audio Processing, International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), 27-29 May 2010, Timisoara, Romania, pp. 431-436. [link]
Context: Another research direction will deal with the adaptive audio filters systematically designed and implemented [19]–[31] using fuzzy control algorithms.
  10 Cited by: C. Pozna, R.E. Precup, N. Minculete, C. Antonya, C.A. Dragos: Properties of Classes, Subclasses and Objects in an Abstraction Model, Proceedings of 19th International Workshop on Robotics in Alpe-Adria-Danube Region RAAD 2010, Budapest, Hungary, 2010, pp. 291-296, IEEE Catalog Number: CFP1075J-CDR, ISBN: 978-1-4244-6884-3.
Context: Another direction of future research will be focused on the integration of the results. Several other models, applications and structures will be used [20]–[32].
  11 Cited by: C.A. Dragos, S. Preitl, R.E. Precup, D. Pirlea, C.S. Nes, E.M. Petriu, C. Pozna: Modeling of a Vehicle with Continuously Variable Transmission, Proceedings of 19th International Workshop on Robotics in Alpe-Adria-Danube Region RAAD 2010, Budapest, Hungary, 2010, pp. 441-446, IEEE Catalog Number: CFP1075J-CDR, ISBN: 978-1-4244-6884-3.
Context: Several models and control system structures will be considered [8]–[16] aiming at the achievement of very good control system performance including fast and bumpless dynamics; zero steady-state control errors will be targeted as well.
  12 Cited by: R.-E. Precup, R.-C. David, S. Preitl, E. M. Petriu, Design aspects of optimal PI controllers with reduced sensitivity for a class of servo systems using PSO algorithms, Facta Universitatis, Series Automatic Control and Robotics, University of Nis (Serbia), ISSN 1820-6417, vol. 8, no. 1, 2009, pp. 1 - 12.
Context: With this regard the introduction of the empirical optimization indices in the objective function will represent a direction of future research to be applied in various control systems applications [25]–[33].
  13 Cited by: C.-A. Dragos, S. Preitl, R.-E. Precup, R.-G. Bulzan, E. M. Petriu, J. K. Tar, Experiments in Fuzzy Control of a Magnetic Levitation System Laboratory Equipment, Proceedings of 8th IEEE International Symposium on Intelligent Systems and Informatics SISY 2010, Subotica, Serbia, 2010, pp. 601-606, ISBN: 978-1-4244-7395-3, IEEE Catalog Number: CFP1084C-CDR.
Context: The rule interpolation can be applied for incomplete or sparse rule bases [16].
  14 Cited by: C. Pozna, L.-T. Koczy, R.-E. Precup, A. Ballagi, A Kantian Pattern of Knowledge, the Observation Representation, Proceedings of 8th IEEE International Symposium on Intelligent Systems and Informatics SISY 2010, Subotica, Serbia, 2010, pp. 405-412, ISBN: 978-1-4244-7395-3, IEEE Catalog Number: CFP1084C-CDR.
Context: Use will be made of different AI tools including fuzzy logic and neural networks and other models [22]–[33].
  15 Cited by: R.-E. Precup, C.-A. Dragos, S. Preitl, M.-B. Radac, E. M. Petriu, Tensor Product Models for Automotive Applications, Proceedings of 14th International Conference on System Theory and Control, Craiova, Romania, 2010, Editura Universitaria Craiova Publishers, Craiova, Romania, ISSN 2068-0465, pp. 405-410.
Context: Future research will be focused on the combination of several control solutions using other models and system structures [31]–[34] while implementing low- cost automation solutions assisted by analyses [35]–[39].
  16 Cited by: R.-E. Precup, C.-A. Dragos, S. Preitl, E. M. Petriu, M.-B. Radac, A. S. Paul, Stable fuzzy control of an electromagnetic actuated clutch, Annals of the University of Craiova, Series: Automation, Computers, Electronics and Mechatronics, vol. 7 (34), no. 1, Oct. 2010, pp. 53-60, ISSN 1841-0626.
Context: It should be pointed out that the models presented in this section can be applied with no major difficulties to other controlled plants and controller structures (... Johanyák and Kovács, 2006; ...).
  17 Cited by: S. Preitl, R.-E. Precup, Z. Preitl, Aspects Concerning the Tuning of 2-DOF Fuzzy Controllers, Proceedings of Xth Triennial International SAUM Conference on Systems, Automatic Control and Measurements SAUM 2010, Nis, Serbia, ISBN 978-86-6125-020-0, pp. 210-219.
Context: is the large time constant. Such processes are used as con- trolled plants in control systems themselves or in local control systems in a wide area of applications [24]–[41].
  18 Cited by: S. Preitl, R.-E. Precup, C.-A. Dragos, M.-B. Radac, Tuning of 2-DOF Fuzzy PI(D) Controllers. Laboratory Applications, Proceedings of 11th IEEE International Symposium on Computational Intelligence and Informatics (CINTI 2010), Budapest, Hungary, 2010, pp. 237-242, ISBN 978-1-4244-9278-7, IEEE Catalog Number CFP1024M-PRT.
Context: The experience of the CS designer can be taken into consideration but the stability analysis can be performed, too. Several stability analysis approaches can be applied in this context [15]–[17], and a special attention should be paid to the definition of the rule base [18], [19].
7 Cited paper: Johanyák, Zs. Cs., Kovács, Sz.: Fuzzy Rule Interpolation by the Least Squares Method, 7th International Symposium of Hungarian Researchers on Computational Intelligence (HUCI 2006), November 24-25, 2006 Budapest, ISBN 963 7154 54 X, pp. 495-506. [pdf
  1 Cited by: Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182. [pdf]
Context:  
This paper tries to fill this gap by analyzing the computational complexity of three single rule reasoning methods (SURE-p [3], SURE-LS [4] and REVE [6]) that were developed for the second step of the generalized methodology of fuzzy rule interpolation (GM) [1]. ...
...The SURE-LS method proposed by Johanyák and Kovács in [4] was originally developed as a tool for the second step of the FRI method LESFRI [4]. Thus it is complement of the set interpolation technique FEAT-LS [4].
 
   2 Cited by: Krizsán, Z.:Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Context: Relevant members of the two-step FRI methods are the techniques suggested by Baranyi, Kóczy and Gedeon [1], the LESFRI [11], FRIPOC [3] and VEIN [12] developed by Johanyák and Kovács as well as the IGRV proposed by Huang and Shen [2].
 
  3 Cited by: A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16.
Context: Relevant members of this family are among others the techniques ... the LESFRI that uses the method of least squares worked out by Johanyák and Kovács [11], and the FRIPOC introduced by Johanyák and Kovács [13].
We used the fuzzy model identification technique RBE-SI [11][14], and the inference method FRIPOC [13].
8 Cited paper: Johanyák Zs. Cs.: Fuzzy szabály-interpolációs módszerek és mintaadatok alapján történő automatikus rendszergenerálás, PhD disszertáció, Hatvany József Informatikai Tudományok Doktori Iskola, Miskolci Egyetem, Miskolc, 2007. [pdf]
  1 Cited by: Krizsán, Z.: Complexity examination of three single rule reasoning methods, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182. [pdf]
Context: The complexity of this correction is NPC (the complete correction method details can be found in [7]), where NPC is the number of polar cuts.
 
  2 Cited by: Drenyovszki, R.: Távolságmértékek a fuzzy szabály-interpolációban, XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 69-72. [pdf]
Context
Ritka szabálybázisra épülő rendszerben a hagyományos kompozíciós következtetési módszerek (Zadeh [8], Mamdani [6], stb.) segítségével nem tudunk helyes eredményeket előállítani minden lehetséges bemenő érték esetén [1][3][4][5]. Ilyenkor általában fuzzy szabály-interpoláción alapuló eljárásokat alkalmaznak, ahol a fuzzy halmazok hasonlóságának és sorrendjének vizsgálata is szükséges. ...
...Ennek a távolság meghatározásnak a hátránya a nagy számításigény mellett az, hogy csak részleges rendezést biztosít a halmazok között [3], ...
...Az eljárás bevezetése egyszerűsítette a számításokat, azonban itt is könnyen előfordulhat olyan eset, amikor a két középvonal metszi egymást, lehetetlenné téve a sorrend meghatározást [3]. ...
...E feladatot legtöbbször a mag középpontja látja el (pl. [4]), de léteznek a tömegközéppontot vagy éppen a tartó középpontját alkalmazó módszerek is [3]. ...
...A referencia pont alapú távolságmérés széles körű elterjedése annak köszönhető, hogy alacsony számításigény mellett biztosítható a halmazok teljes rendezése [3]
 

  3 Cited by: Gál, L. and Kóczy, L.T.: Advanced Bacterial Memetic Algorithms, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 225-243. [link]
Context:
One can find several approaches in the literature for fuzzy model identification (e.g. [23]). Some of them determine the rules and the corresponding linguistic terms based on fuzzy clustering (e.g. the method proposed in [14] or ACP in [10]).
 
  4 Cited by: J. Botzheim, L. Gál and L.T. Kóczy: Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms, In: Studies in Computational Intelligence, Recent Advances in Decision Making, Springer, Berlin/Heidelberg, 2009, DOI: 10.1007/978-3-642-02187-9_3, pp. 21-43.
Context: One can find several approaches in the literature for fuzzy model identification (e.g. [22]). Some of them determine the rules and the corresponding linguistic terms based on fuzzy clustering (e.g. the method proposed in [13] or ACP in [9]). Another group of methods (e.g. RBE-DSS and RBE-SI [10]) start with two initial rules that describe the maximum and minimum of the output and extend the rule base iteratively in course of the tuning. Most of the methods mentioned above are also able to identify fuzzy models with low complexity by generating sparse rule bases.
  5 Cited by: A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16
Context: RBE applies the considerations outlined in [9] and permits a part of the two fuzzy sets of the consequent partition „to hang out” from the minimal and maximal interval of the consequent partition.
During the definition of the fuzzy sets some constraints [9] also have to be taken into consideration. These can cause the modification of the width values.
Finally one applies the constraints required by the parameterization of the fuzzy sets [9].
  6 Cited by: Z. Krizsán, S. Kovács: Gradient-based Consequent Optimization of a FRI Rule Base, Production Systems and Information Engineering, Vol 5 (2009), pp. 177-188.
Context: The main goal of this example is to compare the optimized FIVE system to the system generated by the RBE-DSS (introduced by Johanyák in [11]) method.
 
9 Cited paper: Johanyák, Zs. Cs., Kovács, Sz.:The effect of different fuzzy partition parameterization strategies in gradient descent parameter identification, 4th International Symposium on Applied Computational Intelligence and Informatics (SACI 2007), May 17-18, 2007 Timisoara, Romania, pp. 141-146.
  1 Cited by: Precup, R. E.,Tomescu, M. L. and Preitl, St. : Rule base modification of Takagi-Sugeno fuzzy logic controllers to guarantee system stability, Bulletins for applied & Computer mathematics (BAM), CXII/2008 Nr. 2363, ISSN 0133-3526, pp. 113-120.
Context: If some sample input-output data is given e.g. the gradient descent based parameter optimization method introduced in [6] is also applicable for the fuzzy rule base identification.
 

  2 Cited by: Rădac, M.-B., Precup, R.-E., Preitl, S., Tar, J.K., Fodor, J. and Petriu, E.M.: Gain-Scheduling and Iterative Feedback Tuning of PI Controllers for Longitudinal Slip Control, IEEE 6th International Conference on Computational Cybernetics, November 27-29, 2008, Stara Lesná, Slovakia, pp. 183-188.
Context: Future research will be focused on online parameter estimation, Iterative Learning Control, robust control design and adaptive control using fuzzy and neuro-fuzzy structures aiming application-oriented lowcost solutions. They should be accompanied by adequate analyses [16–20].
 
  3 Cited by: A. S. Paul, R.-E. Precup, J. Fodor and M.-B. Radac: New Experimental Setups for Audio Signal Processing, 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009),May 28–29, 2009, Timişoara, Romania, pp. 405-410.
Context: Another research direction will deal with the adaptive audio filters systematically designed and implemented using fuzzy control algorithms [22-27].
 
  4 Cited by: R.C. David, M.B. Rădac, S. Preitl and J.K. Tar: Particle Swarm Optimization-Based Design of Control Systems with Reduced Sensitivity, 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009),May 28–29, 2009, Timişoara, Romania, pp. 491-496.
Context: The future research will be dedicated to extending the PSO algorithms to other sensitivity-based optimization problems in the time domain [7] and frequency domain. PSO-based optimal fuzzy control systems with reduced sensitivity will be developed and applied in several control system structures [15], [18], [21]–[23].
 
  5 Cited by: A.S. Paul , R.E. Precup , J. Fodor and M.B. Rădac: Software Issues in Experimental Setups for Audio Signal Processing, Scientific Bulletin of “Politehnica” University of Timisoara, Romania, Transactions on Automatic Control and Computer Science, Vol. 54 (68), Fasc. 1, 2009, ISSN 1224-600X, pp. 31-38.
Context: Another research direction will deal with the adaptive audio filters systematically designed and implemented using fuzzy control algorithms [17-23].
10 Cited paper: Johanyák, Zs. Cs., Szabolcsi, J.: Experiences of teaching visual programming with C# and Visual Studio 2005, Pollack Periodica, Vol. 2, Suppl., 2007, pp.97-105. [link]
  1 Cited by: Illés, A.:Microsoft Visual Programming Language a szoftverfejlesztés oktatásban , XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 131-134. [pdf]
Context: Tóth és Johanyák [3] a versenyszféra igényeiből és a közismert szoftverfejlesztési hibákból kiindulva a hangsúlyt a szoftvertechnológiai alapokra és a csoportmunkára helyezve kereste a kiutat. Johanyák és Szabolcsi [1] a gyors alkalmazásfejlesztési technikák és a Visual Studio – C# eszközpáros által nyújtott vizuális programozás-támogatás oktatásba történő bevezetéséről számolt be.
 
  2 Cited by: A. Peculea, V. Dadarlat, I. Ignat, B. Iancu, L. Cobarzan: On developing a QoS framework with self-adaptive bandwidth reconfiguration, Pollack Periodica, 2009, Vol. 4, No. 1, pp. 121–129. [link]
Context: By using the development tool, the proposed end-to-end QoS framework - built using C#.NET [7], will be efficiently tested using an experimental methodology, rather than simulation techniques.
 
11 Cited paper: Johanyák, Zs. Cs., Tóth, Gy. F.: Vizuális módszerek oktatásának hatása a hallgatók programozási hibáira, Matematika-, fizika- és számítástechnika oktatók XXXI. konferenciája, Dunaújváros, 2007. augusztus 23-25., pp. 126-131. [pdf]
  1 Cited by: Illés, A.:Microsoft Visual Programming Language a szoftverfejlesztés oktatásban , XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 131-134. [pdf]
Context: Egy későbbi felmérés kiértékelése [2] kimutatta, hogy bár az új megoldások javulást eredményeztek és bizonyos programozási hibák eltűnéséhez vezettek, azonban összességében hatásuk elmaradt a várakozásoktól.
 
12 Cited paper: Tóth, Gy. F., Johanyák, Zs. Cs.: Teaching software engineering - Experiences and new Approaches, XIX Didmattech 2006, September 6-7, Komarno, Slovakia , ISBN 978-80-89234-23-3 , 261-265.
  1 Cited by: Illés, A.:Microsoft Visual Programming Language a szoftverfejlesztés oktatásban , XIII. Fiatal Műszakiak Tudományos Ülésszaka, Kolozsvár, 2008. március 14-15, pp. 131-134. [pdf]
Context: Tóth és Johanyák [3] a versenyszféra igényeiből és a közismert szoftverfejlesztési hibákból kiindulva a hangsúlyt a szoftvertechnológiai alapokra és a csoportmunkára helyezve kereste a kiutat. Johanyák és Szabolcsi [1] a gyors alkalmazásfejlesztési technikák és a Visual Studio – C# eszközpáros által nyújtott vizuális programozás-támogatás oktatásba történő bevezetéséről számolt be.
 
13 Cited paper: Johanyák Zs. Cs.: Számítógéppel segített hibamód és -hatás elemzés, microCAD 94 - International Computer Science Conference, Miskolc, 1994. március 3., 60-67. old. [pdf]
  1 Cited by: Antal M. R., Kovács Zs.: A FMEA (Hibamód- és Hatás Elemzés) módszer alkalmaz-hatósága a bútorok tervezésénél előforduló hibák megelőzésénél, Faipar HU ISSN: 0014–6897 , L. évfolyam 3. szám, 2002. szeptember, 3-8 old. [pdf]
Context: Az 1. ábra bemutatja az FMEA folyamatát (Johanyák 1994).
 
  2 Cited by: Antal M. R.: Exkluzív bútorok meghatározó formáinak elemzése a használati és esztétikai funkciók optmális arányainak kialakítása szempontjából, Doktori (PhD) értekezés, Nyugat-Magyarországi Egyetem, Faipari Mérnöki Kar, Cziráki József Faanyagtudomány és Technológiák Doktori Iskola, 2007.
Context: A 7. ábra bemutatja az FMEA folyamatát [21. Johanyák 1998].
 
14 Cited paper: Kerekes L., Johanyák Zs. Cs.: Ipari varrógépek konstrukciós FMEA vizsgálata, Műszaki szemle ISSN 1454-0746, 1998/1-2., Erdélyi Magyar Műszaki Tudományos Társaság, Kolozsvár, 31-34. old. [pdf]
  1 Cited by:  Tánczos Vilmos, Tőkés Gyöngyvér: Tizenkét év : Összefoglaló tanulmányok az erdélyi magyar tudományos kutatások 1990-2001 közötti eredményeiről, I. kötet, Kolozsvár : Scientia, 2002, (Sapientia könyvek. Tudománytörténet ; 8-10.), ISBN 973 85422 8 6 [link]
Context: Ezzel magyarázható, hogy az utóbbi időben mind élénkebben tanulmányozzák a minőségbiztosítást, egyre több tudományos tanácskozáson hallunk róla, s növekszik a nyomtatásban megjelent vonatkozó tanulmányok száma is (Fazakas J. 2001; Kerekes L. et al 1997 és 1999; Olaru-Kerekes L. 1999; Kerekes L. 1993, 1994b, 1994a, 1998, 1999; Jeschke-Kerekes L. 1996; Kerekes L.-Sándor 1996; Kerekes L.-Johanyák 1998).
 
15 Cited paper: Johanyák, Zs. Cs., Kovács Sz.: Fuzzy Set Approximation by Weighted Least Squares regression, Annals of the Faculty of Engineering Hunedoara 2006, Tome IV, Fascicule 1, ISSN 1584-2665, pp. 27-34. [pdf]
  1 Cited by:Krizsán, Z.:Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Context: We examine in this paper the uniform time complexity of two FSI methods: FEAT-LS introduced by Johanyák and Kovács in [4] and FEAT-p proposed by Johanyák and Kovács in [5]. ...
...The Fuzzy sEt interpolation based on weighted Least Squares (FEAT-LS) developed by Johanyák and Kovács [4] was originally developed for the widely popular case of shape selection when all the known fuzzy sets belong to the same shape type...
...Some applicable weighting functions are presented e.g. in [4] and [5]. ...
 
 16 Cited paper: Johanyák, Zs. Cs. and Kovács, Sz.: Fuzzy set approximation using polar co-ordinates and linguistic term shifting, 4rd Slovakian-Hungarian Joint Symposium on Applied Machine Intelligence (SAMI 2006), Herl'any, Slovakia, 2006, pp. 219-227. [pdf]
  1 Cited by: Krizsán, Z.:Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Context: We examine in this paper the uniform time complexity of two FSI methods: FEAT-LS introduced by Johanyák and Kovács in [4] and FEAT-p proposed by Johanyák and Kovács in [5]. ...
...Some applicable weighting functions are presented e.g. in [4] and [5]. ...
...Both of the FSI methods being analyzed in this paper applied the concept of Linguistic Term Shifting (LTS) proposed by Johanyák and Kovács in [5]. ...
...The Fuzzy sEt interpolation based on polar cuts (FEAT-p) proposed by Johanyák and Kovács [5] calculates the shape of the new set by its polar cuts. ...
...Fig. 2 Polar cut [5]
 
  2 Cited by: A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16.
Context: The basic idea of the linguistic value shifting [12] is that first the reference points of all known fuzzy sets are determined.
17 Cited paper: Johanyák, Zs. Cs., Kovács, Sz.: Sparse Fuzzy System Generation by Rule Base Extension, 11th IEEE International Conference of Intelligent Engineering Systems (IEEE INES 2007), June 29 - July 1, 2007, Budapest, ISBN 1-4244-1148-3, pp. 99-104.
  1 Cited by: Krizsán, Z.:Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Context: Secondly FSI methods are used by some fuzzy model identification methods (e.g. RBE-SI [6]) that apply the concept of Rule Base Extension (RBE) [6].
 
  2 Cited by: Gál, L. and Kóczy, L.T.: Advanced Bacterial Memetic Algorithms, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 225-243. [link]
Context:
Another group of methods (e.g. RBE-DSS and RBE-SI [11]) start with two initial rules that describe the maximum and minimum of the output and extend the rule base iteratively in course of the tuning.
  3 Cited by: R.-E. Precup, S. Preitl, E.M. Petriu, J.K. Tar, M.-B. Radac, C.-A. Dragos, "Stable Design of Fuzzy Controllers for Robotic Telemanipulation Applications," Proc. 2009 IEEE Workshop on Computational Intelligence in Virtual Environments, pp. 1-6, Nashville. TN, USA, April 2009.
Context: Future work will focus on the generalization of the IFT approach to state-feedback control systems and state observers and MIMO plants. Other applications will also be studied [18-24].
  4 Cited by: M.L.Tomescu, R.E. Precup, S. Preitl, S. Blazic: Elements of Intelligence in Control of a Class of Nonlinear Time-Varying Systems, Proceedings of the International Symposium - Research and Education in Innovation Era, Section Mathematics and Computer Science, 2nd Edition, Ed. Universităţii „Aurel Vlaicu” din Arad, Arad (2008), ISSN 2065-2569, pp. 221-233.
Context: Further research will be dedicated to offering other low cost fuzzy solutions for chaotic systems based on similar approaches and applications [4,19,5,16,17].
  5 Cited by: C. Pozna, R.E. Precup: Modeling Derived from Bayesian Filtering: Analysis of Estimation Process, 13th International Conference on Intelligent Engineering Systems (INES 2009), April 16-18, 2009, Barbados, ISBN 978-1-4244-4113-6, pp. 73-78.
Context: This involves the exponential growth of artificial intelligence techniques in modeling such as fuzzy logic [4,5], neural networks [6], genetic algorithms [7,8], data mining [9,10], etc., and their merge resulting in hybrid models [11-15].
  6 Cited by: M.B. Rădac, R.E. Precup, E.M. Petriu, S. Preitl, C.A. Dragoş, Iterative Feedback Tuning Approach to a Class of State Feedback-Controlled Servo Systems, Proceedings of 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009), Milan, Italy, July 2-5, 2009, ISBN 978-989-8111-99-9, vol. 1 Intelligent Control Systems and Optimization, pp. 41-48.
Context: The future research will be focused on: the consideration of more complex objective functions to include the control signal, the state and output sensitivity functions as well, the generalization to nonlinear processes (Cottenceau et al., 2001; Johanyák and Kovács,...
  7 Cited by: R.-E. Precup, M.-B. Rădac, S. Preitl, M.-L. Tomescu, E. M. Petriu, A. S. Paul, IFT-Based PI-Fuzzy Controllers: Signal Processing and Implementation, Proceedings of 6th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2009), Milan, Italy, July 2-5, 2009, ISBN 978-989-8111-99-9, vol. 1 Intelligent Control Systems and Optimization, pp. 207-212.
Context: The rule base of the PI-FC can be reduced to two rules (Johanyák and Kovács, 2007).
  8 Cited by: J. Botzheim, L. Gál and L.T. Kóczy: Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms, In: Studies in Computational Intelligence, Recent Advances in Decision Making, Springer, Berlin/Heidelberg, 2009, DOI: 10.1007/978-3-642-02187-9_3, pp. 21-43.
Context: One can find several approaches in the literature for fuzzy model identification (e.g. [22]). Some of them determine the rules and the corresponding linguistic terms based on fuzzy clustering (e.g. the method proposed in [13] or ACP in [9]). Another group of methods (e.g. RBE-DSS and RBE-SI [10]) start with two initial rules that describe the maximum and minimum of the output and extend the rule base iteratively in course of the tuning. Most of the methods mentioned above are also able to identify fuzzy models with low complexity by generating sparse rule bases.
  9 Cited by: R.-E. Precup, C. Gavriluta, M.-B. Radac, S. Preitl, C.-A. Dragos, J. K. Tar, E. M. Petriu, Iterative Learning Control Experimental Results for Inverted Pendulum Crane Mode Control, Proceedings of 7th International Symposium on Intelligent Systems and Informatics SISY 2009, Subotica (Serbia), ISBN 978-1-4244-5349-8, IEEE Catalog Number: CFP0984C-CDR, 2009, pp. 323 - 328.
Context: Besides the real-time applications (design and implementation) of the ILCAs to other controlled plants (including nonlinear benchmarks) controlled by conventional and fuzzy CS structures will be treated [16]–[25].
  10 Cited by: R.-E. Precup, M.-L. Tomescu, St. Preitl, Fuzzy Logic Control System Stability Analysis Based on Lyapunov's Direct Method, International Journal of Computers, Communications & Control (Agora University Editing House - CCC Publications), ISSN 1841-9836, E-ISSN 1841-9844, Vol. IV (2009), No. 4, pp. 415-426. [link]
Context: The stabilityanalysis algorithm suggested in this paper can be applied also when the rule base (2) of the T-SFLC is not complete. However interpolationtechniques [10,19] are needed in the implementation of the T-SFLC.
ISI Web of Science link
  11 Cited by: R.-E. Precup, M.-B. Radac, St. Preitl, E. M. Petriu, C.-A. Dragos, Iterative Feedback Tuning in Linear and Fuzzy Control Systems, "Towards Intelligent Engineering and Information Technology", editors: I. J. Rudas, J. Fodor, J. Kacprzyk, Studies in Computational Intelligence, vol. 243, Springer-Verlag, Berlin, Heidelberg, ISBN 978-3-642-03736-8, ISSN 1860-949X (Print) 1860-9503 (Online), 2009, pp. 179 - 192.
Context: The number of rules in the complete rule base (18) can be reduced further for the sake of low-cost computing. Interpolation techniques can be applied with this regard (Johanyák and Kovács 2007). The additional parameter η with typical values within 0 < η < 1 has been introduced in (18) to alleviate the overshoot of the control system when e(k) and Δe(k) have the same sign.
  12 Cited by: R.E. Precup, S. Preitl, E.M. Petriu, J.K. Tar, M.L. Tomescu, C. Pozna, Generic two-degree-of-freedom linear and fuzzy controllers for integral processes, Journal of The Franklin Institute, vol. 346, no. 10, pp. 988-1003, Dec. 2009.
Context: The key element in Fig. 2 is the basic four inputs – two outputs fuzzy controller (B-FC) that represents a Takagi–Sugeno fuzzy system. It makes use of the MAX and MIN operators in the inference engine and it employs the weighted sum method for defuzzification [45], [46] and [47].
ISI Web of Science link
  13 Cited by: A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16.
Context: Extend the rule base by applying the concept of Rule Base Extension [14].
The concept of Rule Base Extension (RBE) [14] suggests the creation of a fuzzy system in two steps.
The RBE concept [14] extends the rule base in course of an iterative process.
So far two methods based on RBE have been developed: Rule Base Extension based on Default Set Shapes (RBE-DSS) and Rule Base Extension based on Set Interpolation (RBE-SI) [14].
We used Root Mean Square Error in Percentage (RMSEP) as performance index (PI), which is the most proper according to [14] for this task.
  14 Cited by: C.A. Dragos, S. Preitl, M.B. Radac, R.E. Precup: Nonlinear and Linearized Models and Low-cost Control Solution for an Electromagnetic Actuator, 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009), May 28-29, 2009, Timisoara, Romania, pp. 89-94.
Context: Other control structures with different controllers can be used [6], [17], analyzed in [7]. Future research will deal with low-cost fuzzy control solutions [18]–[21].
  15 Cited by: M.B. Radac, R.E. Precup, S. Preitl and C.A. Dragos: Iterative Feedback Tuning in MIMO Systems. Signal Processing and Application, 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009), May 28-29, 2009, Timisoara, Romania, pp. 77-82.
Context: Future research will be focused on solving the limitations outlined before. The applications in fuzzy logic control schemes will be tackled [15]–[20].
  16 Cited by: C.A. Dragoş, S. Preitl and R.E. Precup: Low-cost Takagi-Sugeno Fuzzy Controller for an Electromagnetic Actuator, Scientific Bulletin of “Politehnica” University of Timisoara, Romania, Transactions on Automatic Control and Computer Science, Vol. 54 (68), Fasc. 2, 2009, ISSN 1224-600X, pp. 87-92
Context: The rules represent input-output linear relations of the nonlinear system [17],[18],[19]. The design of the two TS-FCs is based on the linear continuous controller with the following t.f. [13], [19],[20] ...
  17 Cited by: M.B. Rădac, R.E. Precup,S. Preitl, E.M. Petriu, C.A. Dragoş, A.S. Paul and S. Kilyeni: Signal Processing Aspects in State Feedback Control Based on Iterative Feedback Tuning, Proceedings of the 2nd conference on Human System Interactions (HSI 2009), Catania, Italy, 2009, ISBN:978-1-4244-3959-1, pp. 37-42.
Context: The analysis of the convergence of IFT algorithms should be done in all cases and application including those concerning the control of nonlinear plants [15]–[22].
  18 Cited by: C.-A. Dragos, S. Preitl, R.-E. Precup, M. Cretiu and J. Fodor, Modern Control Solutions with Applications in Mechatronic Systems. In: Computational Intelligence in Engineering, I. J. Rudas, J. Fodor and J. Kacprzyk (Eds.), Springer-Verlag, Berlin, Heidelberg, Studies in Computational Intelligence, vol. 313, pp. 87-102, 2010, ISBN 978-3-642-15219-1, ISSN 1860-949X.
Context: Future research will be dedicated to other controller models. Real-time experimen- tal tests are necessary in all applications [19]–[25] beyond the automotive mechatronic systems.
Springer link
  19 Cited by: K. Balázs, J. Botzheim, L. Kóczy: Comparative Analysis of Interpolative and Non-interpolative Fuzzy Rule Based Machine Learning Systems Applying Various Numerical Optimization Methods, in Proceedings of WCCI 2010 IEEE World Congree on Computational Intelligence, July, 18-23, 2010 - CCIB, Barcelona, Spain, pp. pp. 1 - 8.
Context: Since not only the theory, but the application of interpolative fuzzy systems is growing in the fuzzy community (see e.g. [1]–[4]), our present work aims to in vestigate and ...
IEEE Xplore link
18 Cited paper: Johanyák, Zs. Cs., Kovács, Sz.: A brief survey on fuzzy set interpolation methods, Doktoranduszok Fóruma, Miskolci Egyetem, 9 November 2006, pp. 72-77. [pdf]
  1 Cited by: Krizsán, Z.:Uniform Complexity of Feat-p and Feat-ls Fuzzy Set Interpolation Method, MicroCAD 2008, International Scientific Conference, Section N:Applied Information Engineering, 20-21 March 2008, Miskolc, pp. 83-88.
Context: Although several characteristics of the FSI methods have been studied (e.g. in [13]) the uniform time complexity, an important feature of the techniques has not been covered by the previous research work.
 
19 Cited paper: Johanyák E., Johanyák Zs. Cs.(1997) Az ISO 9000 utat nyit a teljeskörű minőségirányítás felé, A Gépipari és Automatizálási Műszaki Főiskola Közleményei, XIII. Évfolyam 1996-1997., Kecskemét, 1997, ISSN 0230-6182,pp.  31-38. [pdf]
  1 Cited by: Berecz, A., Kriskó, E.: Az e-learning minőségbiztosítási megközelítései és alkalmazásuk a GDF ILIAS-ban, Informatika a Felsőoktatásban, Debrecen, 2008 augusztus 27-28, ISBN 978-963-473-129-0, pp. 1-13. [pdf]
Context: A minőséget a TQM (Total Quality Management) modell gyengeség-központúan mindvégig a „vevőre” koncentrálva, a dolgozók teljes körű részvétele mellett kívánja elérni úgy, hogy a tanulást társadalmi méretűvé szélesíti. Mindehhez az eredmények terjesztésével és benchmarking technikák alkalmazásával él, amelyek közül alább néhányat mi is áttekintünk. (Johanyák, 1997)
 
20 Cited paper: Johanyák, Zs. Cs. and Alvarez Gil, R. P. : Generalization of the single rule reasoning method SURE-LS for the case of arbitrary polygonal shaped fuzzy sets, Annals of the Faculty of Engineering Hunedoara, ISSN 1584-2665, Tome VI (2008), Fascicule 2, pp. 161-170.  [link]
  1 Cited by: Morioka, K., Kovács, S., Korondi, P., Lee, J.-H., Hashimoto, H.: Adaptive Camera Selection Based On Fuzzy Automaton For Object Tracking In A Multicamera System, Annals of Faculty of Engineering Hunedoara, Tome VI (2008), Fascicule 1, (ISSN 1584 – 2665), pp. 177-182.
Context: In case of fuzzy rule interpolation [11], [12], [17], the above heuristic can be simply implemented by the state-transition fuzzy rule base [13], [14] as shown in Table.1.
 
21 Cited paper: Johanyák, Zs. Cs.: Fuzzy logika, oktatási segédlet, KF GAMF Kar, Informatika Tanszék, Kecskemét, 2004.  [pdf]
  1 Cited by: Hauszmann János: Kockázat és megbízhatóság a menedzsmentben, PhD értekezés, BME Menedzsment és Vállalatgazdaságtan Tanszék, Budapest, 2006. [pdf]
Context: Végül a szabály "réseket" valamilyen szabályközelítéssel (lineáris interpoláció, szabályok lineáris extrapolációja, stb.) fedik el. [28]
 
22 Cited paper: Johanyák, Zs. Cs.: Vague Environment Based Set Interpolation, A GAMF Közleményei, Kecskemét, XXI. évfolyam (2006-2007), ISSN 0230-6182, pp. 33-44.
  1 Cited by: Vincze, D. and Kovács, S.: Using fuzzy rule interpolation based automata for controlling navigation and collision avoidance behaviour of a robot, IEEE 6th International Conference on Computational Cybernetics, November 27-29, 2008, Stara Lesná, Slovakia, pp. 79-84.
Context: For example FRIPOC (Johanyák and Kovács [25]) extends the range of the applicable membership functions types by introducing the concept of polar cuts using a polar coordinate system, LESFRI (Johanyák and Kovács [26]) preserves the characteristic shape type of the antecedent and consequent partitions by applying the method of least squares, and VEIN [27] solves the task of rule interpolation in the vague environment by the help of the set interpolation method VESI proposed by Johanyák in [12].
 
23 Cited paper: Johanyák, Zs. Cs., Tikk, D., Kovács, Sz. and Wong, K. K.: Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox, Proc. of the IEEE World Congress on Computational Intelligence (WCCI'06), 15th Int. Conf. on Fuzzy Systems (FUZZ-IEEE'06), July 16--21, 2006, Vancouver, BC, Canada, pp. 1427-1433. [Bibtex] [IEEExplore link]
  1 Cited by: Shyi-Ming Chen and Yuan-Kai Ko: Fuzzy Interpolative Reasoning for Sparse Fuzzy Rule-Based Systems Based on alpha-Cuts and Transformations Techniques, : IEEE Transactions on Fuzzy Systems, Dec. 2008, Volume: 16, Issue: 6, pp. 1626-1648., ISSN: 1063-6706 [link]
Context: ...  we also use the Matlab FRI toolbox [7] to get the fuzzy interpolative reasoning consequences of the MACI method...
... where the Matlab FRI toolbox [7] implementation of the MACI method [17] and the IMUL method [19] only can handle triangular fuzzy and trapezoidal fuzzy sets...
 
ISI Web of Science link
  2 Cited by: Gál, L. and Kóczy, L.T.: Advanced Bacterial Memetic Algorithms, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 225-243. [link]
Context: Recently a freely available comprehensive FRI toolbox [13] and an FRI oriented web site (fri.gamf.hu) were appeared for aiding and guiding the future FRI applications.
 
  3 Cited by: J. Botzheim, L. Gál and L.T. Kóczy: Fuzzy Rule Base Model Identification by Bacterial Memetic Algorithms, In: Studies in Computational Intelligence, Recent Advances in Decision Making, Springer, Berlin/Heidelberg, 2009, DOI: 10.1007/978-3-642-02187-9_3, pp. 21-43.
Context: Recently a freely available comprehensive FRI toolbox [13] and an FRI oriented web site (fri.gamf.hu) were appeared for aiding and guiding the future FRI applications.
 
  4 Cited by: S. Preitl, R.E. Precup, M.L. Tomescu, M.B. Radac, E.M. Petriu, C.A. Dragos, Model-Based Design Issues in Fuzzy Logic Control, "Towards Intelligent Engineering and Information Technology", editors: I. J. Rudas, J. Fodor, J. Kacprzyk, Studies in Computational Intelligence, vol. 243, Springer-Verlag, Berlin, Heidelberg, ISBN 978-3-642-03736-8, ISSN 1860-949X (Print) 1860-9503 (Online), 2009, pp. 137 - 152
Context: The model-based design of fuzzy logic control system is applied for Mamdani and TS FLCs as well. Much research on the model-based design of fuzzy logic control systems making use of TS fuzzy models has been carried out in the recent years (Sun and Wang 2006), (Johanyák et al. 2006), (Blažič and Škrjanc 2007), (Oblak et al. 2007), (Pang and Lur 2008), (Vaščák 2008), (Tanaka et al. 2009), (Yuan and Wang 2009).
 
  5 Cited by: A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16.
Context: We used RMSEP as a performance index and calculated the performance index with the Fuzzy Rule Interpolation (FRI) Matlab Toolbox [17].
 
  6 Cited by: C. Pozna, V. Prahovean, R.-E. Precup: A New Pattern of Knowledge Based on Experimenting the Causality Relation, Proceedings of 14th International Conference on Intelligent Engineering Systems INES 2010, Las Palmas of Gran Canaria, Spain, 2010, pp. 61-66, ISBN 978-1-4244-7651-0. [link]
Context: Use will be made of different AI tools including fuzzy logic and neural networks [19] – [28].
  7 Cited by: C.A. Dragos, S. Preitl, R.E. Precup, R.G. Bulzan, C. Pozna, J.K. Tar: Takagi-Sugeno Fuzzy Controller for a Magnetic Levitation System Laboratory Equipment, International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), 27-29 May 2010, Timisoara, Romania, pp. 55-60. [link]
Context: The new control solutions given in this paper are designed by the development of our previous design methods [6]–[8] and of the other popular fuzzy control and logic approaches [9]–[17].
  8 Cited by: S. Biro, R.E. Precup and D. Todinca: Double inverted pendulum control by linear quadratic regulator and reinforcement learning, International Joint Conference on Computational Cybernetics and Technical Informatics (ICCC-CONTI), 27-29 May 2010, Timisoara, Romania, pp. 159-164. [link]
Context: This idea is adapted from the application developed in [20] for a single inverted pendulum system, and it has the potential for generalization to be applicable to other models and algorithms [21]–[25].
  9 Cited by: C. Pozna, R.E. Precup, N. Minculete, C. Antonya, C.A. Dragos: Properties of Classes, Subclasses and Objects in an Abstraction Model, Proceedings of 19th International Workshop on Robotics in Alpe-Adria-Danube Region RAAD 2010, Budapest, Hungary, 2010, pp. 291-296, IEEE Catalog Number: CFP1075J-CDR, ISBN: 978-1-4244-6884-3.
Context: Another direction of future research will be focused on the integration of the results. Several other models, applications and structures will be used [20]–[32].
  10 Cited by: R.E. Precup, C. Borchescu, M.B. Radac, S. Preitl, C.A. Dragos, E. M. Petriu, J. K. Tar, Implementation and Signal Processing Aspects of Iterative Regression Tuning, Proceedings of the 2010 IEEE International Symposium on Industrial Electronics (ISIE 2010), Bary, Italy, 2010, IEEE Catalog Number: CFP10ISI-CDR, ISBN: 978-1-4244-6391-6, pp. 1657-1662.
Context: The second-order servo systems with integral component can be viewed as particular benchmark systems in wide application areas [16]–[23] where ensuring very good CS performance indices by means of low-cost automation solutions is challenging.
  11 Cited by: R.-E. Precup, R.-C. David, S. Preitl, E. M. Petriu, Design aspects of optimal PI controllers with reduced sensitivity for a class of servo systems using PSO algorithms, Facta Universitatis, Series Automatic Control and Robotics, University of Nis (Serbia), ISSN 1820-6417, vol. 8, no. 1, 2009, pp. 1 - 12.
Context: With this regard the introduction of the empirical optimization indices in the objective function will represent a direction of future research to be applied in various control systems applications [25]–[33].
  12 Cited by: C. Pozna, L.-T. Koczy, R.-E. Precup, A. Ballagi, A Kantian Pattern of Knowledge, the Observation Representation, Proceedings of 8th IEEE International Symposium on Intelligent Systems and Informatics SISY 2010, Subotica, Serbia, 2010, pp. 405-412, ISBN: 978-1-4244-7395-3, IEEE Catalog Number: CFP1084C-CDR.
Context: Use will be made of different AI tools including fuzzy logic and neural networks and other models [22]–[33].
  13 Cited by: C.-A. Dragos, R.-E. Precup, S. Preitl, E. M. Petriu., M.-B. Radac, Control Solutions, Simulation and Experimental Results for a Magnetic Levitation Laboratory System, Proceedings of 7th EUROSIM Congress on Modelling and Simulation (EUROSIM 2010), Prague, Czech Republic, 2010, Editors: M. Snorek, Z. Buk, M. Cepek, J. Drchal, ISBN 978-80-01-04589-3, Vol. 2: Full Papers (CD), paper index 155, 8 pp.
Context: Such solutions can be implemented easily in other control system structures and models [13,14,15,16,17,18,19,20,21,22, 23,24].
  14 Cited by:S. Blažič: Takagi-Sugeno vs. Lyapunov-based tracking control for a wheeled mobile robot, WSEAS TRANSACTIONS on SYSTEMS and CONTROL (ISSN: 1991-8763), Issue 8, Volume 5, August 2010, pp. 667-676.
Context: The use of fuzzy techniques has been widely accepted for the control of nonlinear systems [19,2,21,20,8,26,27,16,22].
  15 Cited by: R.E. Precup, M.L. Tomescu, S. Preitl, E.M. Petriu: Fuzzy Logic-based Stabilization of Nonlinear Time-Varying Systems, International Journal of Artificial Intelligence, Autumn 2009, Vol. 3, No. A09, ISSN 0974-0635, pp. 24-36.
Context: In recent years, much research on the fuzzy model based design has been carried out on the basis of the T-S fuzzy models (Skrjanc et al., 2005), (Sun and Wang, 2006), (Johanyák et al., 2006), ...
  16 Cited by: C.-A. Dragos, S. Preitl, R.-E. Precup, C.-S. Nes, E. M. Petriu, G. Tirtea, One- and Two-Degree-of-Freedom Fuzzy Control of an Eletromgnetic Actuated Clutch, Proceedings of 14th International Conference on System Theory and Control, Craiova, Romania, 2010, Editura Universitaria Craiova Publishers, Craiova, Romania, ISSN 2068-0465, pp. 190-195.
Context: The one-degree-of-freedom (1 DOF) and 2 DOF controllers prove to be successful in many other applications [6]–[19].
  17 Cited by: S. Preitl, R.-E. Precup, Z. Preitl, Aspects Concerning the Tuning of 2-DOF Fuzzy Controllers, Proceedings of Xth Triennial International SAUM Conference on Systems, Automatic Control and Measurements SAUM 2010, Nis, Serbia, ISBN 978-86-6125-020-0, pp. 210-219.
Context: is the large time constant. Such processes are used as con- trolled plants in control systems themselves or in local control systems in a wide area of applications [24]–[41].
24 Cited paper: Johanyák, Zs. Cs. and Szabó, A.: Tool life modelling using RBE-DSS method and LESFRI inference mechanism, A GAMF Közleményei, Kecskemét, XXII. (2008), ISSN 0230-6182, pp. 17-28.
  1 Cited by: Kovács, Sz.: Fuzzy Rule Interpolation from a Practical Point of View, Acta Technica Jaurinensis, Series Intelligentia Computatorica, Vol. 1. No. 3., 2008, pp. 83-101. [link]
Context: FRI methods have been also successfully applied in several other areas like fuzzy modeling of an anaerobic tapered fluidized bed reactor (Johanyák et al. [9]) or tool life modeling (Johanyák and Szabó [10]).
 
  2 Cited by: A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16
Context: Numerous models have been developed for modeling of tool life in case of milling, e.g. exponential [28], Taylor [28], corrected Taylor [28], Gilbert [6], Kronenberg [22], Rule Base Extension based on Default Set Shapes (LESFRI+RBE-DSS) [16] etc.
In the course of the tool life modeling for DA20 and DA25 we used results obtained by milling experiments carried out by carbide inserts DA20 and DA25 and published in [16]: ... In [16] the fuzzy models generated by LESFRI+RBE-DSS are compared with results computed by exponential, Taylor and corrected Taylor methods [28]. In [16] the overall evaluation proved that the fuzzy system gives a better approximation of the measured data in the case of both carbide insert types and the fuzzy result in the case of DA20 was much better between the input and output than in the case of DA25.
 
  3 Cited by: Z. Krizsán, S. Kovács: Gradient-based Consequent Optimization of a FRI Rule Base, Production Systems and Information Engineering, Vol 5 (2009), pp. 177-188.
Context: For example Johanyák, Parthiban and Sekaran [2] developed fuzzy models for an anaerobic tapered fluidized bed reactor, Johanyák and Szabó
25 Cited paper: Johanyák, Z. C. and Ádámné, M. A.: Fuzzy Modeling of the Relation between Components of Thermoplastic Composites and their Mechanical Properties, Proceedings of the 5th International Symposium on Applied Computational Intelligence and Informatics (SACI 2009), May 28-29, 2009, Timisoara, Romania, pp. 481-486.
  1 Cited by: C.A. Dragos, S. Preitl, R.E. Precup, Model Predictive Control Solutions for an Electromagnetic Actuator, Proceedings of 7th International Symposium on Intelligent Systems and Informatics SISY 2009, Subotica (Serbia), ISBN 978-1-4244-5349-8, IEEE Catalog Number: CFP0984C-CDR, 2009, pp. 59 - 64.
Context: Digital simulations for the accepted numerical values sustain the good results and the usefulness of the predictive control solutions for servosystems in practice and prepare the further application to other models and controller structures [26]–[30].
 
  2 Cited by: Claudiu Pozna, Radu-Emil Precup, Jozsef K. Tar, Igor Skrjanc, Stefan Preitl, New results in modelling derived from Bayesian filtering, Knowledge-Based Systems, vol. 23, no. 2, pp. 182-194, March 2010.
Context: This involves the exponential growth of non-conventional modelling based on knowledge-based systems(KBS)tools including fuzzy logic [5,26,29,46,50,55],neural networks ...
 
ISI Web of Science link
26 Cited paper: Johanyák, Z. C.: Sparse Fuzzy Model Identification Matlab Toolbox - RuleMaker Toolbox, IEEE 6th International Conference on Computational Cybernetics, November 27-29, 2008, Stara Lesná, Slovakia, pp. 69-74.
  1 Cited by: A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16
Context: We made separate models for the two carbide insert types using the Sparse Fuzzy Model Identification (SFMI) Matlab ToolBox [10] and applying RBE-SI for rule base generation and FRIPOC for fuzzy reasoning.
  2 Cited by: D Vincze, S Kovács: Incremental Rule Base Creation with Fuzzy Rule Interpolation-Based Q-Learning, in I.J. Rudas et al. (Eds.):Studies in Computational Intelligence, 2010, Volume 313, Computational Intelligence in Engineering, pp. 191-203
Context:
Springer link
  3 Cited by: K. Balázs, J. Botzheim, L. Kóczy: Comparative Analysis of Interpolative and Non-interpolative Fuzzy Rule Based Machine Learning Systems Applying Various Numerical Optimization Methods, in Proceedings of WCCI 2010 IEEE World Congree on Computational Intelligence, July, 18-23, 2010 - CCIB, Barcelona, Spain, pp. pp. 1 - 8.
Context: Since not only the theory, but the application of interpolative fuzzy systems is growing in the fuzzy community (see e.g. [1]–[4]), our present work aims to in vestigate and ...
IEEE Xplore link
27 Cited paper: Johanyák, Z. C., Kovács, S.: Polar-cut Based Fuzzy Model for Petrophysical Properties Prediction, SCIENTIFIC BULLETIN of “Politehnica” University of Timisoara, ROMANIA, Transactions on AUTOMATIC CONTROL and COMPUTER SCIENCE, ISSN 1224-600X, Vol: 57(67) No: 24/ 2008, pp. 195-200.
  1 Cited by: A. Berecz: Fuzzy Rule Interpolation-based Tool Life Modeling Using RBE-SI and FRIPOC, 1st International Workshop on Computational Intelligence in Information Sciences - a Pre-Symposium Workshop of SACI 2009 (5th International Symposium on Applied Computational Intelligence and Informatics, May 28-29, 2009 Timisoara, Romania), Miskolc, Hungary, May 25-26, 2009, pp. 11-16
Context: In order to solve this complexity problem sparse (not covering) rule bases and reasoning methods based on rule interpolation can be applied [15].
We used the fuzzy model identification technique RBE-SI [11][14], and the inference method FRIPOC [13]. FRIPOC had been used before (e.g. in [15] but RBE-SI has not been applied for real life problems so far.
28 Cited paper: Johanyák, Z. C., Kovács, S.: Fuzzy modeling of Petrophysical Properties Prediction Applying RBE-DSS and LESFRI, International Symposium on Logistics and Industrial Informatics (LINDI 2007), September 13-15, 2007, Wildau, Germany, pp. 87-92.
  1 Cited by: W.E.-S. Afify and A.H.I. Hassan: Permeability and Porosity Prediction from Wireline logs Using Neuro-Fuzzy Technique, Ozean Journal of Applied Sciences 3(1), 2010, ISSN 1943-2429, pp. 157-175. [pdf]
Context: With the emergence of intelligent techniques that combine ANN and fuzzy together have been applied successfully in well log analysis [Huang et al., 2001, Kadkhodaie Ilkhchi et al., 2008, Khaxar et al., 2007, Johanyák et al.2007].
  2 Cited by: R.E. Precup, M.L. Tomescu, S. Preitl, E.M. Petriu: Fuzzy Logic-based Stabilization of a Magnetic Ball Suspension System, International Journal of Artificial Intelligence (IJAI), Autumn 2010, Vol. 5, No. A10, ISSN 0974-0635, pp. 56-66
Context: It must be tested by many real-worls and quite different case studies (...,Johanyák and Kovács, 2007, ...)
29 Cited paper: Johanyák, Zs. Cs., Kovács Sz.: Neuro-fuzzy módszerek alkalmazása a kísérletmódszertanban, A GAMF Közleményei, Kecskemét, XX. évfolyam (2005), ISSN 0230-6182, pp. 37-48.
  1 Cited by: Varga Tamás: A fuzzy logika alklmazási lehetőségei a minőségtetrvezésben, Debreceni Műszaki Közlemények, 2010/1, HU ISSN 2060 - 6869, pp. 43-51
Context: Johanyák bemutatja [3] közleményében, hogyan lehet a kísérletie eredmények egy a hagyományostól eltérő feldolgozási lehtőségét alkalmazni egy adaptív neuro-fuzzy rendszerrel modellezve a folyamatot.
30 Cited paper: Johanyák, Zs. Cs., Dr. Kovács Sz.: A fuzzy tagsági függvény megválasztásáról, A GAMF Közleményei, Kecskemét, XIX. évfolyam (2004), ISSN 0230-6182, pp. 73-84.
  1 Cited by: G. Molnárka: Fuzzy Expert System as a Decision Support Tool in the Visual Examination Process in Building Diagnostics,CIB 2010 World Congress, May, 2010, Manchester, UK, pp. 1-15.
Context: Note: in the present state of the recommended fuzzy expert system – in favour of simplifying the calculations – fuzzy membership functions decomposable into linear spans (triangular and trapezoidal) are applied (Johanyák & Kovács, 2004), which form Ruspini partitions. [link]
  2 Cited by: G. Molnárka: Management of Uncertainty in Visual Examination Procedure in Building Diagnostics with Fuzzy Expert System, in Proceedings of ISCIII'09 4th International Symposium on Computational Intelligence and Intelligent Informatics, 2009, Luxor, Egypt, pp. 31-40.
Context: Note: in the present state of the recommended fuzzy expert system – in favour of simplifying the calculations – fuzzy membership functions decomposable into linear spans (triangular and trapezoidal) are applied [23], which form Ruspini partitions. [Link]