Simultaneous Perturbation Stochastic Approximation Algorithm Combined with Neural Network and Fuzzy Simulation

被引：0
|
作者：
宁玉富 [1 ]
唐万生 [1 ]
郭长友 [2 ]
机构：
[1] Institute of Systems Engineering, Tianjin University
[2] Department of Computer Science, Dezhou University
来源：
Transactions of Tianjin University | 2008年 / 01期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
fuzzy variable; fuzzy programming; fuzzy simulation; neural network; approximation theory; perturbation techniques; computer simulation; simultaneous perturbation stochastic approximation algorithm;
D O I：
暂无
中图分类号：
TP183 [人工神经网络与计算];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码：43 / 49
页数：7
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