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Simultaneous perturbation stochastic approximation algorithm combined with neural network and fuzzy simulation

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Abstract

In order to solve three kinds of fuzzy programming models, i.e. fuzzy expected value model, fuzzy chance-constrained programming model, and fuzzy dependent-chance programming model, a simultaneous perturbation stochastic approximation algorithm is proposed by integrating neural network with fuzzy simulation. At first, fuzzy simulation is used to generate a set of input-output data. Then a neural network is trained according to the set. Finally, the trained neural network is embedded in simultaneous perturbation stochastic approximation algorithm. Simultaneous perturbation stochastic approximation algorithm is used to search the optimal solution. Two numerical examples are presented to illustrate the effectiveness of the proposed algorithm.

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Authors and Affiliations

  1. Institute of Systems Engineering, Tianjin University, Tianjin, 300072, China

    Yufu Ning & Wansheng Tang

  2. Department of Computer Science, Dezhou University, Dezhou, 253023, China

    Yufu Ning & Changyou Guo

Authors
  1. Yufu Ning
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  2. Wansheng Tang
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  3. Changyou Guo
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Corresponding author

Correspondence to Yufu Ning.

Additional information

Supported by National Natural Science Foundation of China (No.70471049) and China Postdoctoral Science Foundation (No. 20060400704).

NING Yufu, born in 1967, male, Dr, associate Prof.

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Ning, Y., Tang, W. & Guo, C. Simultaneous perturbation stochastic approximation algorithm combined with neural network and fuzzy simulation. Trans. Tianjin Univ. 14, 43–49 (2008). https://doi.org/10.1007/s12209-008-0009-7

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  • DOI: https://doi.org/10.1007/s12209-008-0009-7

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