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A Three-stage method for designing Genetic Fuzzy Systems by learning from examples

  • Applications of Evolutionary Computation Evolutionary Computation in Machine Learning, Neural Networks, and Fuzzy Systems
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Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

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Abstract

In this paper, we present a three step method for designing Genetic Fuzzy Systems combining an iterative and increasing rule derivation stage and two genetic-based simplification and tuning processes. The performance of the method proposed is shown by measuring the accuracy of the Fuzzy Logic Controllers designed in the fuzzy modeling of two three-dimensional control surfaces and comparing them with others generated by using Wang and Mendel's method, one of the most widely known iterative rule derivation processes.

This research has been partially supported by DGICYT PB92-0933

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Authors and Affiliations

  1. Dept. of Computer Science and Artificial Intelligence, E.T.S. de Ingeniería Informática, University of Granada, 18071, Granada, Spain

    Oscar Cordón, Francisco Herrera & Manuel Lozano

Authors
  1. Oscar Cordón
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  2. Francisco Herrera
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  3. Manuel Lozano
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Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

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© 1996 Springer-Verlag Berlin Heidelberg

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Cordón, O., Herrera, F., Lozano, M. (1996). A Three-stage method for designing Genetic Fuzzy Systems by learning from examples. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1035

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  • DOI: https://doi.org/10.1007/3-540-61723-X_1035

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

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