摘要
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 pro-gramming 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.
In order to solve three kinds of fuzzy programm model, fuzzy chance-constrained programming mode ng models, i.e. fuzzy expected value 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.
基金
National Natural Science Foundation of China (No.70471049)
China Postdoctoral Science Foundation (No. 20060400704)
关键词
模糊变量
模糊程序
模糊算法
神经网络
近似理论
fuzzy variable
fuzzy programming
fuzzy simulation
neural network
approximation theory
perturbation techniques computer simulation simultaneous perturbation stochasticapproximation algorithm