TSK fuzzy model using kernel-based fuzzy c-means clustering

In order to overcome the dimension problem of the traditional fuzzy clustering, we use kernel-based fuzzy c-means clustering (KFCM) to construct first-order TSK fuzzy models. The proposed algorithm is composed of two phases. In the first phase, the antecedent fuzzy sets are obtained by KFCM. We pres...

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Bibliographische Detailangaben

Hauptverfasser:	Qianfeng Cai, Wei Liu
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Clustering algorithms Computational complexity Fuzzy sets Kernel Least squares approximation Least squares methods Polynomials Prototypes Robustness Shape
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