The bi-objective fuzzy c-means cluster analysis for TSK fuzzy system identification

For conventional fuzzy clustering-based approaches to fuzzy system identification, a fuzzy function is used for cluster formation and another fuzzy function is used for cluster validation to determine the number and location of the clusters which define IF parts of the rule base. However, the different fuzzy functions used for cluster formation and validation may not indicate the same best number and location of the clusters. This potential disparity motivates us to propose a new fuzzy clustering-based approach to fuzzy system identification based on the bi-objective fuzzy c-means (BOFCM) cluster analysis. In this approach, we use the BOFCM function for both cluster formation and validation to simultaneously determine the number and location of the clusters which we hope can efficiently and effectively define IF parts of the rule base. The proposed approach is validated by applying it to the truck backer-upper problem with an obstacle in the center of the field.

[1]  Doheon Lee,et al.  On cluster validity index for estimation of the optimal number of fuzzy clusters , 2004, Pattern Recognit..

[2]  Ferenc Szeifert,et al.  Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models , 2002, IEEE Trans. Syst. Man Cybern. Part B.

[3]  H. Ishibuchi.  DEVELOPMENT OF FUZZY NEURAL NETWORKS , 1996 .

[4]  Antonio F. Gómez-Skarmeta,et al.  A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling , 1997, IEEE Trans. Fuzzy Syst..

[5]  V. J. Rayward-Smith,et al.  Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , 1999 .

[6]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[7]  M. Delgado,et al.  An inductive learning procedure to identify fuzzy systems , 1993 .

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

[9]  Hsiao-Fan Wang,et al.  Bi-criteria fuzzy c -means analysis , 1994 .

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

[11]  C. J. Kim,et al.  An algorithmic approach for fuzzy inference , 1997, IEEE Trans. Fuzzy Syst..

[12]  Yu-Geng Xi,et al.  A clustering algorithm for fuzzy model identification , 1998, Fuzzy Sets Syst..

[13]  Hiroyuki Watanabe,et al.  Application of a fuzzy discrimination analysis for diagnosis of valvular heart disease , 1994, IEEE Trans. Fuzzy Syst..

[14]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Sankar K. Pal,et al.  Fuzzy models for pattern recognition , 1992 .

[16]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[17]  Oscar Cordón,et al.  On the combination of fuzzy logic and evolutionary computation: a short review and bibliography , 1997 .

[18]  S. Fang,et al.  A two-phase approach to fuzzy system identification , 2003 .

[19]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[20]  Euntai Kim,et al.  A new approach to fuzzy modeling , 1997, IEEE Trans. Fuzzy Syst..

[21]  Tzung-Pei Hong,et al.  Induction of fuzzy rules and membership functions from training examples , 1996, Fuzzy Sets Syst..

[22]  Kyung-Whan Oh,et al.  A validity measure for fuzzy clustering and its use in selecting optimal number of clusters , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[23]  L. A. Zadeh,et al.  From Circuit Theory to System Theory , 1962, Proceedings of the IRE.

[24]  Lawrence O. Hall,et al.  Generating fuzzy rules from data , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

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

[26]  Noureddine Zahid,et al.  A new cluster-validity for fuzzy clustering , 1999, Pattern Recognit..