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机构地区:[1]Institute of Systems Engineering, Tianjin University [2]Department of Computer Science, Dezhou University
出 处:《Transactions of Tianjin University》2008年第1期43-49,共7页天津大学学报(英文版)
基 金:National Natural Science Foundation of China (No.70471049);China Postdoctoral Science Foundation (No. 20060400704)
摘 要: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.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.
关 键 词:fuzzy variable fuzzy programming fuzzy simulation neural network approximation theory perturbation techniques computer simulation simultaneous perturbation stochasticapproximation algorithm
分 类 号:TP183[自动化与计算机技术—控制理论与控制工程]
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