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2003 | 13 | 2 | 215-223

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A fuzzy if-then rule-based nonlinear classifier


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This paper introduces a new classifier design method that is based on a modification of the classical Ho-Kashyap procedure. The proposed method uses the absolute error, rather than the squared error, to design a linear classifier. Additionally, easy control of the generalization ability and robustness to outliers are obtained. Next, an extension to a nonlinear classifier by the mixture-of-experts technique is presented. Each expert is represented by a fuzzy if-then rule in the Takagi-Sugeno-Kang form. Finally, examples are given to demonstrate the validity of the introduced method.








Opis fizyczny




  • Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland


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