A new objective function for fuzzy c-regression model and its application to T-S fuzzy model identification

This paper proposes a new objective function for fuzzy c-regression model (FCRM) clustering algorithm. The main motivation for this work is to develop an identification procedure for nonlinear systems affected by measurement noise. The proposed methodology is based to adding a second regularization term in the objective function of FCRM clustering algorithm in order to take in account the data are noisy. the orthogonal least square is used to identify the consequent parameters. A comparative study is presented. Validation results involving simulation of the identification of nonlinear benchmark problems have demonstrated the effectiveness and practicality of the proposed algorithm.