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基于PSO求解随机期望值模型的混合智能算法
引用本文:肖宁,曾建潮,李卫斌. 基于PSO求解随机期望值模型的混合智能算法[J]. 计算机工程与应用, 2009, 45(10): 45-48. DOI: 10.3778/j.issn.1002-8331.2009.10.014
作者姓名:肖宁  曾建潮  李卫斌
作者单位:太原科技大学,系统仿真与计算机应用研究所,804,太原,030024;咸阳师范学院,计算机科学系,陕西,咸阳,712000
摘    要:随机期望值模型是一类有着广泛应用背景的随机规划问题,为了寻找更为高效的求解随机期望值模型的算法,采用随机仿真产生样本训练BP网络以逼近随机函数,然后应用微粒群算法并以逼近随机函数的神经元网络作为适应值估计和实现为了检验解的可行性,从而提出了一种求解随机期望值模型的混合智能算法。最后通过两个实例的仿真结果说明了算法的正确性和有效性。

关 键 词:随机规划  随机期望值模型  微粒群算法  随机仿真  神经网络
收稿时间:2007-10-10
修稿时间:2008-12-29 

Solving stochastic expected value models with hybrid intelligent algorithm of based on PSO
XIAO Ning,ZENG Jian-chao,LI Wei-bin. Solving stochastic expected value models with hybrid intelligent algorithm of based on PSO[J]. Computer Engineering and Applications, 2009, 45(10): 45-48. DOI: 10.3778/j.issn.1002-8331.2009.10.014
Authors:XIAO Ning  ZENG Jian-chao  LI Wei-bin
Affiliation:1.Division of System Simulation & Computer Application,Taiyuan University of Science and Technology,Taiyuan 030024,China 2.Computer Science Department,Xianyang Normal University,Xianyang,Shaanxi 712000,China
Abstract:The stochastic expected value model belongs to a class of stochastic programming problems,which has wide application backgrounds,in order to search an algorithm which can solve this problem effectively,in the paper,random simulation is used to produce training samples for BP neural network to approximate the stochastic function.And a hybrid intelligent algorithm for stochastic expected value models combined particle swarm optimization with BP neural network for approximation of the fitness function and chec...
Keywords:stochastic programming  stochastic expected value models  particle swarm optimization  random simulation  neural networks
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