The Takagi-Sugeno fuzzy model identification method of parameter varying systems

Abstract

This paper presents the TS model identification method by which a great number of systems whose parameters vary dramatically with working states can be identified via Fuzzy Neural Networks (FNN). The suggested method could overcome the drawbacks of traditional linear system identification methods which are only effective under certain narrow working states and provide global dynamic description based on which further control of such systems may be carried out. Simulation results of a second-order parameter varying system demonstrate the effectiveness of the method.