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

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.

Keywords

fuzzy variable / fuzzy programming / fuzzy simulation / neural network / approximation theory / perturbation techniques / computer simulation / simultaneous perturbation stochastic approximation algorithm

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