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On Designing of Flexible Neuro-Fuzzy Systems for Nonlinear Modelling

  • Krzysztof Cpałka
  • Olga Rebrova
  • Robert Nowicki
  • Leszek Rutkowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6743)

Abstract

In the paper the evolutionary strategy is used for learning of neuro-fuzzy structures of a Mamdani type applied to modelling of nonlinear systems. In the process of evolution we determine parameters of fuzzy membership functions, specific t-norm in a fuzzy inference, specific t-norm for aggregation of antecedents in each rule, and specific t-conorm describing an aggregation operator. The method is tested using well known approximation benchmarks.

Keywords

Fuzzy System Fuzzy Membership Function Aggregation Operator Temporary Population Triangular Norm 
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 2011

Authors and Affiliations

  • Krzysztof Cpałka
    • 1
    • 2
  • Olga Rebrova
    • 3
  • Robert Nowicki
    • 1
    • 2
  • Leszek Rutkowski
    • 1
    • 2
  1. 1.Department of Computer EngineeringCzestochowa University of TechnologyPoland
  2. 2.Institute of Information TechnologyAcademy of Management (SWSPiZ)Poland
  3. 3.Institute of PharmaeconomicsThe Russian State Medical UniversityRussia

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