Identification of Nonlinear System Using Fuzzy Wiener Model through Self-adaptive Differential Evolution Algorithm *

In this paper, we focus on nonlinear system identification through fuzzy Wiener model(FWM), which consists of a linear dynamic subsystem followed by a static Takagi-Sugeno (T-S) fuzzy model. The identification of nonlinear system is accomplished by estimating the parameters of FWM through minimizing the error between the output of nonlinear system and that of FWM. An improved differential evolution algorithm, i.e., self-adaptive differential evolution algorithm, is adopted to estimate the parameters of FWM simultaneously. Case studies are given to illustrate the effectiveness of the proposed method.