Fuzzy neural modeling via clustering and support vector machines

This paper describes a novel fuzzy rule-based modeling approach for some industrial processes. Structure identification is realized by clustering and support vector machines. When the process is slow, fuzzy rules can be obtained automatically. Parameters identification uses the techniques of fuzzy neural networks. A time-varying learning rate assures stability of the modeling error.

[1]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[2]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[4]  Jacek M. Leski,et al.  TSK-fuzzy modeling based on /spl epsiv/-insensitive learning , 2005, IEEE Transactions on Fuzzy Systems.

[5]  Chin-Teng Lin,et al.  Dynamic optimal learning rates of a certain class of fuzzy neural networks and its applications with genetic algorithm , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[6]  Jung-Hsien Chiang,et al.  Support vector learning mechanism for fuzzy rule-based modeling: a new approach , 2004, IEEE Transactions on Fuzzy Systems.

[7]  Jung-Hsien Chiang,et al.  Support vector learning mechanism for fuzzy rule-based modeling: a new approach , 2004, IEEE Trans. Fuzzy Syst..

[8]  Shyi-Ming Chen,et al.  Fuzzy backward reasoning using fuzzy Petri nets , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[10]  Spyros G. Tzafestas,et al.  NeuroFAST: on-line neuro-fuzzy ART-based structure and parameter learning TSK model , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[11]  Léon Personnaz,et al.  Neural-network construction and selection in nonlinear modeling , 2003, IEEE Trans. Neural Networks.

[12]  Anuradha M. Annaswamy,et al.  Robust Adaptive Control , 1984, 1984 American Control Conference.

[13]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[14]  Xiaoou Li,et al.  Fuzzy identification using fuzzy neural networks with stable learning algorithms , 2004, IEEE Transactions on Fuzzy Systems.

[15]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control , 1994 .

[16]  M. Razaz,et al.  A normalized gradient descent algorithm for nonlinear adaptive filters using a gradient adaptive step size , 2001, IEEE Signal Processing Letters.

[17]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[18]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[19]  Xiaoou Li,et al.  Some new results on system identification with dynamic neural networks , 2001, IEEE Trans. Neural Networks.

[20]  Snehasis Mukhopadhyay,et al.  Adaptive control using neural networks and approximate models , 1997, IEEE Trans. Neural Networks.

[21]  Plamen P. Angelov,et al.  An approach for fuzzy rule-base adaptation using on-line clustering , 2004, Int. J. Approx. Reason..