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Fuzzy Controller Generation with a Fuzzy Classification Method

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Computational Intelligence (Fuzzy Days 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1625))

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Abstract

Fuzzy controller generating procedures when using crisp input-output data produce the necessary system in two steps: first they produce a starting rule set and then they tune the parameters that influence the approximation with a learning algorithm. Other solutions work under special conditions as hybrid neuro-fuzzy systems improving the approximation with a gradient based learning algorithm (e.g. in the case of monotonous membership functions), or use the methods of the genetic algorithms to generate the fuzzy controller. This article demonstrates a new method which reduces the problem to a classification task and carries out the generation of the rules and the tuning of the system in a single step.

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Author information

Authors and Affiliations

  1. Faculty of Business and Economics, Janus Pannonius University, H-7621, Pécs, Rákóczi út 80

    István Borgulya

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  1. István Borgulya
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Editor information

Editors and Affiliations

  1. Computer Science I, University of Dortmund, D-44221, Dortmund, Germany

    Bernd Reusch

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© 1999 Springer-Verlag Berlin Heidelberg

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Borgulya, I. (1999). Fuzzy Controller Generation with a Fuzzy Classification Method. In: Reusch, B. (eds) Computational Intelligence. Fuzzy Days 1999. Lecture Notes in Computer Science, vol 1625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48774-3_9

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  • DOI: https://doi.org/10.1007/3-540-48774-3_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66050-7

  • Online ISBN: 978-3-540-48774-6

  • eBook Packages: Springer Book Archive

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