| 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]. |
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| 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]. |
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| 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. [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: Johanyák and Kovács also published and applied in [5] a specialized condition set aiming the characterization of single rule reasoning methods. |
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| 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. [draft version] | ||
| 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]. |
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| 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]). |
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| 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. |
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| 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]). |
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| 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. [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: Johanyák and Kovács [2] survey and evaluate a wider range of FRI methods based on a self defined application oriented evaluation criteria set. |
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| 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]. |
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| 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] |
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| 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] |
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| 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]. |
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| 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. |
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| 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]. |
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| 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]: ... |
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| 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) |
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| 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. |
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| 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]. |
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| 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]). |
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| 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]). |
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| 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. |
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| 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]. |
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| 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]. |
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| 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. |
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| 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]. |
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| 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. |
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| 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]. |
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| 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]. |
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| 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]. |
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| 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. |
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| 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]). |
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| 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. |
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| 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]. |
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| 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. [draft pdf] | ||
| 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]. |
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| 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]. |
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| 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]. |
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| 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]. |
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| 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. |
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| 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. |
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| 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. |
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| 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. [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: 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. |
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| 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). |
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| 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). |
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| 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]. ... |
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| 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] |
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| 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. |
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| 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 [draft 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: 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]. |
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| 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. |
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| 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]. |
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| 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]. |
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| 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]. |
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| 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,... |
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| 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). |
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| 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. |
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| 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]. |
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| 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. |
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| 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. |
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| 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]. |
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| 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]. |
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| 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] ... |
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| 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]. |
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| 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. |
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| 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) |
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| 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. [pdf] | ||
| 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. |
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| 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] |
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| 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 [pdf] | ||
| 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]. |
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| 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. |
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| 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. |
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| 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). |
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| 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]. |
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| 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]. |
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| 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]. |
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| 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]. |
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| 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]. |
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| 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. |
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| 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. [pdf] | ||
| 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]). |
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| 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. |
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| 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]. |
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| 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. |
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| 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. |
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| 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]. |
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