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Weighted Fuzzy Genetic Programming Algorithm for Structure and Parameters Selection of Fuzzy Systems for Nonlinear Modelling

  • Krystian ŁapaEmail author
  • Krzysztof Cpałka
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 521)

Abstract

In this paper a weighted fuzzy genetic programming algorithm for selection of structure and parameters of fuzzy systems for nonlinear modelling is proposed. This method is based on fuzzy genetic programming and innovations in this method concern, among the others, using weights of fuzzy aggregation operators, using weights of fuzzy rules, using fitness function criteria designed for fuzzy genetic programming and using dynamic links between fuzzy rules and fuzzy rules base. The proposed method was tested with use of typical nonlinear modelling problems.

Keywords

Genetic programming Weights Fuzzy system Nonlinear modelling Dynamic systems 

Notes

Acknowledgment

The project was financed by the National Science Centre (Poland) on the basis of the decision number DEC-2012/05/B/ST7/02138.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Computational IntelligenceCzęstochowa University of TechnologyCzęstochowaPoland

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