Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization

作     者：李勇 唐英干 

作者机构：Key Laboratory of Network Control and Intelligent Instrument (Ministry of Education) Chongqing University of Posts and Telecommunications Chongqing 400065 Institute of Electrical Engineering Yanshan University Qinhuangdao 066004 

出 版 物：《Chinese Physics Letters》 (中国物理快报（英文版）)

年 卷 期：2010年第27卷第9期

页      面：46-49页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 070201[理学-理论物理] 0704[理学-天文学] 081201[工学-计算机系统结构] 0702[理学-物理学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：PARTICLE swarm optimization CHAOS theory FUZZY systems WIENER'S model (Communication) MATHEMATICAL models NONLINEAR theories COMPUTER simulation 

摘      要：A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi-Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henon and Lozi chaotic system identification show the effectiveness of the proposed method.