A modified fuzzy c-regression model clustering algorithm for T-S fuzzy model identification
Moêz Soltani, Borhen Aissaoui, A Chaari, Fayçal Ben Hmida, Moncef Gossa · 2011
In this paper, a modified fuzzy c-regression model (FCRM) clustering algorithm for identification of Takagi-Sugeno (T-S) fuzzy model is proposed. The FCRM clustering algorithm has considerable sensitive to noise. To overcome this problem, a modified FCRM clustering algorithm is presented. This latter is based to adding a second regularization term in the alternative optimization process of FCRM. This regularization term is introduce in objective function in order to take in account the data are noisy. The parameters of the local linear models are identified based on orthogonal least squares (OLS). The proposed approach is demonstrated by means of the identification of nonlinear numerical examples.