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On-Line Modeling Via Fuzzy Support Vector Machines

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MICAI 2008: Advances in Artificial Intelligence (MICAI 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5317))

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Abstract

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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Author information

Authors and Affiliations

  1. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, México D.F., 07360, México

    Julio César Tovar & Wen Yu

Authors
  1. Julio César Tovar
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  2. Wen Yu
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Editor information

Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, 07738, Mexico City, México

    Alexander Gelbukh

  2. Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro #1 , Sta. María Tonantzintla, 72840, Puebla, México

    Eduardo F. Morales

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© 2008 Springer-Verlag Berlin Heidelberg

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Tovar, J.C., Yu, W. (2008). On-Line Modeling Via Fuzzy Support Vector Machines. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_21

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  • DOI: https://doi.org/10.1007/978-3-540-88636-5_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88635-8

  • Online ISBN: 978-3-540-88636-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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