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Neuro-Fuzzy Methods in Fuzzy Rule Generation

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Fuzzy Sets in Approximate Reasoning and Information Systems

Part of the book series: The Handbooks of Fuzzy Sets Series ((FSHS,volume 5))

Abstract

This chapter illustrates several methods to learn fuzzy rules from data by using approaches known as neuro-fuzzy methods. The learning procedures that are used in this area are mostly motivated by the learning algorithms known from neural networks. In learning fuzzy rules we distinguish between structure learning and parameter learning. The first term refers to finding an initial fuzzy rule base, and the second term denotes the adaptation of parameters of membership functions to enhance the performance of a fuzzy rule base.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, 39106, Magdeburg, Germany

    Detlef Nauck & Rudolf Kruse

Authors
  1. Detlef Nauck
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  2. Rudolf Kruse
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Editor information

Editors and Affiliations

  1. University of West Florida, USA

    James C. Bezdek

  2. IRIT, CNRS & University of Toulouse III, France

    Didier Dubois  & Henri Prade  & 

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Nauck, D., Kruse, R. (1999). Neuro-Fuzzy Methods in Fuzzy Rule Generation. In: Bezdek, J.C., Dubois, D., Prade, H. (eds) Fuzzy Sets in Approximate Reasoning and Information Systems. The Handbooks of Fuzzy Sets Series, vol 5. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5243-7_6

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  • DOI: https://doi.org/10.1007/978-1-4615-5243-7_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7390-2

  • Online ISBN: 978-1-4615-5243-7

  • eBook Packages: Springer Book Archive

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