Modeling via on-line clustering and fuzzy support vector machines for nonlinear system

This paper describes a novel non-linear modeling approach by on-line clustering, fuzzy rules and fuzzy support vector machines. Structure identification is realized by on-line clustering method and support vector machines, and the rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, tue upper bounds of modeling errors are proven‥

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