On-line modeling via fuzzy support vector machines. (English)
Gelbukh, Alexander (ed.) et al., MICAI 2008: Advances in artificial intelligence. 7th Mexican international conference on artificial intelligence, Atizapán de Zaragoza, Mexico, October 27‒31, 2008. Proceedings. Berlin: Springer (ISBN 978-3-540-88635-8/pbk). Lecture Notes in Computer Science 5317. Lecture Notes in Artificial Intelligence, 220-229 (2008).
Summary: This paper describes a novel nonlinear modeling approach by on-line clustering, fuzzy rules and support vector machine. Structure identification is realized by an on-line clustering method and fuzzy support vector machines, the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, the upper bounds of the modeling errors are proven.
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