TS fuzzy model identification by a novel objective function based fuzzy clustering algorithm

A Fuzzy C Regression Model (FCRM) distance metric has been used in Competitive Agglomeration (CA) algorithm to obtain optimal number rules or construct optimal fuzzy subspaces in whole input output space. To construct fuzzy partition matrix in data space, a new objective function has been proposed that can handle geometrical shape of input data distribution and linear functional relationship between input and output feature space variable. Premise and consequence parameters of Takagi-Sugeno (TS) fuzzy model are also obtained from the proposed objective function. Linear coefficients of consequence part have been determined using the Weighted Recursive Least Square (WRLS) framework. Effectiveness of the proposed algorithm has been validated using a nonlinear benchmark model.

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