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Design of hybrid genetic- SVD ANFIS networks for fatigue life modelling of GRP composites

Author

Listed:
  • K. Salmalian

    (The University of Guilan)

  • N. Nariman-Zadeh

    (The University of Guilan)

  • H. Haftchenari

    (The University of Guilan)

  • A. Jamali

    (The University of Guilan)

Abstract

Genetic algorithm (GA) and singular value decomposition (SVD) are deployed for the optimal design of both Gaussian membership functions of antecedents and the vector of linear coefficients of consequents, respectively, of adaptive neurofuzzy inference systems (ANFIS) networks that are used for fatigue life modelling and prediction of unidirectional GRP Composites. The aim of such modelling is to show how the fatigue life varies with the variation of important parameters namely, maximum stress, stress ratio, fiber angle. It is demonstrated that SVD can be effectively used to optimally find the vector of linear coefficients of conclusion parts in ANFIS models and their Gaussian membership functions in premise parts are determined by GA.

Suggested Citation

  • K. Salmalian & N. Nariman-Zadeh & H. Haftchenari & A. Jamali, 2009. "Design of hybrid genetic- SVD ANFIS networks for fatigue life modelling of GRP composites," Fuzzy Information and Engineering, Springer, vol. 1(3), pages 271-287, September.
    • Handle: RePEc:spr:fuzinf:v:1:y:2009:i:3:d:10.1007_s12543-009-0021-1
    DOI: 10.1007/s12543-009-0021-1
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