Fuzzy Identification Method in Nonlinear System Based on G-K Clustering Algorithm

被引：1

作者：

Shi JianZhong ^{[1
]}

Han Pu ^{[2
]}

Jiao SongMing ^{[2
]}

Wang DongFeng ^{[2
]}

机构：

[1] North China Elect Power Univ, Sch Control Sci & Engn, Beijing 102206, Peoples R China

[2] North China Elect Power Univ, Sch Control Sci & Engn, Baoding 071003, Peoples R China

来源：

CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS | 2009年

关键词：

T-S fuzzy model; G-K clustering algorithm; Orthogonal least square; Fuzzy identification;

D O I：

10.1109/CCDC.2009.5195115

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In accordance with the problems that the algorithm is too complex in the past fuzzy modeling, methods, this article propose a new method of fuzzy modeling for nonlinear system. The method is simple and powerful. In this method, the premise configuration and parameter of this fuzzy model is decided by G-K fuzzy clustering algorithm, and succedent parameter of fuzzy model is identified by orthogonal least square. Finally the effectiveness and practicability of this method is demonstrated by the simulation result of the Box-Jenkins gas furnace data.

引用

页码：212 / +

页数：2

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