A Novel Fuzzy Model Identification Approach Based on FCM and Gaussian Membership Function

Abstract

To solve the problem of fuzzy identification of nonlinear systems, a novel fuzzy identification method based on fuzzy c-means clustering (FCM) algorithm and Gaussian function is proposed. Firstly, fuzzy clustering algorithm is used to divide the input space to obtain the clustering center, then the clustering center is used as the gaussian function center to determine the membership function to obtain the premise parameters of the fuzzy model, and the conclusion parameters of the fuzzy model are identified by recursive least squares (RLS). Finally, three simulation examples are given to verify the effectiveness of the proposed method in identifying T-S fuzzy model.