Takagi-Sugeno fuzzy model parameters identification based on fuzzy c-regression model clustering algorithm and particle swarm optimization

A methodology for identification of the parameters of the local linear Takagi-Sugeno fuzzy models using weighted recursive least square is presented in this paper. The weighted recursive least square (WRLS) is sensitive to initialization which leads to no converge. In order to overcome this problem, particle swarm optimization is employed to optimize the initial states of WRLS. This new approach combines the advantages of fuzzy c-regression model clustering algorithm and particle swarm optimization. Validation results involving simulation of the identification of nonlinear systems have demonstrated the effectiveness of the proposed algorithm.