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The fuzzy classifier system: Motivations and first results

  • Classifier Systems And Immune Networks
  • Conference paper
  • First Online:
Parallel Problem Solving from Nature (PPSN 1990)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 496))

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Abstract

This paper presents a proposal for a machine learning system, called the fuzzy classifier system. The fuzzy classifier system allows for inputs, outputs, and internal variables to take continuous values over given ranges. The fuzzy classifier system learns by creating fuzzy rules which relate the values of the input variables to internal or output variables. It has credit assignment and conflict resolution mechanisms which reassemble those of common classifier systems, with a fuzzy nature. The fuzzy classifier system employs a genetic algorithm to evolve adequate fuzzy rules. Preliminary results show that the fuzzy classifier system can effectively create fuzzy rules that imitate the behavior of a simple static system.

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Biblography

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Author information

Authors and Affiliations

  1. Centro de Inteligencia Artificial, Instituto Tecnológico y de Estudios Superiores de Monterrey Sucursal de Correos “J”, C.P. 64849, Monterrey, N.L., Mexico

    Manuel Valenzuela-Rendón

Authors
  1. Manuel Valenzuela-Rendón
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Editor information

Hans-Paul Schwefel Reinhard Männer

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© 1991 Springer-Verlag Berlin Heidelberg

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Valenzuela-Rendón, M. (1991). The fuzzy classifier system: Motivations and first results. In: Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature. PPSN 1990. Lecture Notes in Computer Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0029774

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  • DOI: https://doi.org/10.1007/BFb0029774

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54148-6

  • Online ISBN: 978-3-540-70652-6

  • eBook Packages: Springer Book Archive

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