Fuzzy Neural Network Identification Method Based on Fuzzy Clustersand its Application
JIANG Shan-he, LI Qiang(Physics and Power Engineering Institute, Anqing Normal College, Anqing 246011, China)
In accordance with modified T-S model,an adaptive fuzzy neural network(AFNN) model is proposed. The fuzzy space structure of system and the number of fuzzy rules based on fuzzy competitive learning algorithm are determined and the fitness degree of each rule contrast to each sample is obtained. The parameters of AFNN are on-line identified by means of Kalman filtering algorithm. The proposed AFNN has the simple model structure and the ability of universal approach, and improves greatly the precision of identification. The identified fuzzy model has the advantages of simplicity and effectiveness. The AFNN is applied to the fuzzy identification for a nonlinear system and CSTR.The simulation results show the effectiveness of the proposed method.
【CateGory Index】： TP183