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A Basic Approach to Reduce the Complexity of a Self-generated Fuzzy Rule-Table for Function Approximation by Use of Symbolic Regression in 1D and 2D Cases

  • G. Rubio
  • H. Pomares
  • I. Rojas
  • A. Guillen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3562)

Abstract

There are many papers in the literature that deal with the problem of the design of a fuzzy system from a set of given training examples. Those who get the best approximation accuracy are based on TSK fuzzy rules, which have the problem of not being as interpretable as Mamdany-type Fuzzy Systems. A question now is posed: How can the interpretability of the generated fuzzy rule-table base be increased? A possible response is to try to reduce the rule-base size by generalizing fuzzy-rules consequents which are symbolic functions instead of fixed scalar values or polynomials, and apply symbolic regressions technics in fuzzy system generation. A first approximation to this idea is presented in this paper for 1-D and 2D functions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • G. Rubio
    • 1
  • H. Pomares
    • 1
  • I. Rojas
    • 1
  • A. Guillen
    • 1
  1. 1.Department of Computer Architecture and Computer TechnologyUniversity of GranadaSpain

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