Fuzzy identification method in nonlinear system based on G-K clustering algorithm

Authors:
Shi JianZhong;Han Pu;Jiao SongMing;Wang DongFeng
Affiliations:
School of Control Science and Engineering, North China Electric Power University, Beijing;School of Control Science and Engineering, North China Electric Power University, Baoding;School of Control Science and Engineering, North China Electric Power University, Baoding;School of Control Science and Engineering, North China Electric Power University, Baoding
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Year:
2009

Citing 3
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Abstract

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.