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Evolutionary Learning of Mamdani-Type Neuro-fuzzy Systems

  • Marcin Gabryel
  • Leszek Rutkowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)

Abstract

In this paper we present an evolutionary method for learning fuzzy rule base systems as an alternative to gradient methods. It is known that the backpropagation algorithm can be trapped in local minima. We use evolutionary strategies (μ,λ) with a novel method for generating an initial population. The results of simulations illustrate efficiency of our method.

Keywords

Membership Function Evolutionary Strategy Fuzzy System Individual Rule Evolutionary Learn 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marcin Gabryel
    • 1
  • Leszek Rutkowski
    • 1
    • 2
  1. 1.Department of Computer EngineeringCzęstochowa University of TechnologyCzęstochowaPoland
  2. 2.Department of Artificial IntelligenceWSHE University in ŁódźŁódźPoland

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