Modeling of Fuzzy Control Design for Nonlinear Systems Based on Takagi-Sugeno Method

被引：0

作者：

Tsai, Pu-Sheng ^{[1
]}

Wu, Ter-Feng ^{[2
]}

Hu, Nien-Tsu ^{[3
]}

Chen, Jen-Yang ^{[4
]}

机构：

[1] China Univ Sci & Technol, Dept Elect Engn, Taipei 11581, Taiwan

[2] Natl Ilan Univ, Dept Elect Engn, Ilan 26047, Taiwan

[3] Chung Shan Inst Sci & Technol, Integrated Logist Support Ctr, Taoyuan 32599, Taiwan

[4] Ming Chuan Univ, Dept Elect Engn, Taoyuan 33348, Taiwan

来源：

2014 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING (IIH-MSP 2014) | 2014年

关键词：

Takagi-Sugeno fuzzy systems; nonlinear dynamic systems; fuzzy modeling; LYAPUNOV STABILITY; IDENTIFICATION;

D O I：

10.1109/IIH-MSP.2014.243

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we first develop a procedure for constructing Takagi-Sugeno fuzzy systems from input-output pairs to identify nonlinear dynamic systems. The fuzzy system can approximate any nonlinear continuous function to any arbitrary accuracy that is substantiated by the Stone Weierstrass theorem. A learning-based algorithm is proposed in this paper for the identification of T-S (Takagi-Sugeno) models. Our modeling algorithm contains four blocks: fuzzy C-Mean partition block, LS coarse tuning, fine turning by gradient descent, and emulation block. The ultimate target is to design a fuzzy modeling to meet the requirements of both simplicity and accuracy for the input-output behavior.

引用

页码：970 / 973

页数：4

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