Fuzzy identification method in nonlinear system based on G-K clustering algorithm
Shi JianZhong, Pu Han, Jiao SongMing, Wang DongFeng · 2009
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.