A clustering algorithm for fuzzy model identification

被引：148
|
作者：
Chen, JQ [1 ]
Xi, YG [1 ]
Zhang, ZJ [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Inst Automat, Shanghai 200030, Peoples R China
来源：
FUZZY SETS AND SYSTEMS | 1998年 / 98卷 / 03期
关键词：
cluster analysis; linguistic modeling; nonlinear system identification;
D O I：
10.1016/S0165-0114(96)00384-3
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The fuzzy model proposed by Takagi and Sugeno can represent highly nonlinear systems and is widely used for the representation of fuzzy rules. In this paper, the model is firstly modified to make its identification easier. Based on the fuzzy c-partition space, four criteria are proposed for optimization of the model parameters. Following that, a clustering algorithm composed of fuzzy c-linear functions clustering and like fuzzy c-means clustering is developed for minimizing the four criteria. An identification scheme for rule's premise and consequence parameters is deduced from the clustering algorithm in succession. Finally, four examples are demonstrated to verify the effectiveness of the proposed algorithm. (C) 1998 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：319 / 329
页数：11
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