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A clustering algorithm for fuzzy model identification

✍ Scribed by Jian-Qin Chen; Yu-Geng Xi; Zhong-Jun Zhang

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
741 KB
Volume
98
Category
Article
ISSN
0165-0114

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✦ Synopsis

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. ~

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