Fuzzy system identification based on support vector regression and genetic algorithm

A new fuzzy identification approach using support vector regression (SVR) and genetic algorithm (GA) is presented in this paper. Firstly positive definite reference function is utilised to construct a qualified Mercer kernel for SVR. Then an improved GA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy model. Finally, a set of TS fuzzy rules can be extracted from the SVR directly. Simulation results show that the resulting fuzzy model not only costs less fuzzy rules, but also possesses good generalisation ability.

[1]  Mohamed Chtourou,et al.  Improved constructive learning algorithms for fuzzy inference system identification , 2007, Int. J. Model. Identif. Control..

[2]  Antonio F. Gómez-Skarmeta,et al.  About the use of fuzzy clustering techniques for fuzzy model identification , 1999, Fuzzy Sets Syst..

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

[4]  Bernhard Schölkopf,et al.  The connection between regularization operators and support vector kernels , 1998, Neural Networks.

[5]  Ginalber Luiz de Oliveira Serra,et al.  Methodology for fuzzy identification in a noisy environment , 2006, Int. J. Model. Identif. Control..

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

[7]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[8]  Madasu Hanmandlu,et al.  Structure identification of generalized adaptive neuro-fuzzy inference systems , 2003, IEEE Trans. Fuzzy Syst..

[9]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[10]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[11]  Yixin Chen,et al.  Support vector learning for fuzzy rule-based classification systems , 2003, IEEE Trans. Fuzzy Syst..

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

[13]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[14]  Nostrand Reinhold,et al.  the utility of using the genetic algorithm approach on the problem of Davis, L. (1991), Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York. , 1991 .

[15]  Andreas Bastian.  Identifying fuzzy models utilizing genetic programming , 2000, Fuzzy Sets Syst..

[16]  Chris J. Harris,et al.  On the modelling of nonlinear dynamic systems using support vector neural networks , 2001 .