Title :
A new cluster validity criterion for fuzzy c-regression model and its application to T-S fuzzy model identification
Author :
Kung, Chung-Chun ; Lin, Chih-Chien
Author_Institution :
Dept. of Electr. Eng., Tatung Univ., Taipei, Taiwan
Abstract :
This paper proposes a new cluster validity criterion designed for the fuzzy c-regression model (FCRM) clustering algorithm. The proposed cluster validity criterion is utilized to determine the appropriate number of clusters in the FCRM. A systematic procedure for the T-S fuzzy model identification is proposed based on the FCRM accompanied with the new cluster validity criterion. Simulation results show that for a given nonlinear system, the proposed algorithm can effectively and accurately obtain a T-S fuzzy model for it.
Keywords :
fuzzy control; nonlinear control systems; pattern clustering; regression analysis; T-S fuzzy model identification; cluster validity criterion; fuzzy c-regression model clustering algorithm; nonlinear system; Algorithm design and analysis; Bridges; Clustering algorithms; Electronic mail; Fuzzy systems; Mathematical model; Nonlinear systems; Partitioning algorithms; Piecewise linear techniques; Takagi-Sugeno model;
Conference_Titel :
Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on
Print_ISBN :
0-7803-8353-2
DOI :
10.1109/FUZZY.2004.1375432
