A Hybrid Intelligent Algorithm for Stochastic Dependent-Chance Programming

Author

Ning, Xiao ; Jianchao, Zeng

Author_Institution

Taiyuan Univ. of Sci. & Technol., Taiyuan

fYear

2008

fDate

23-24 Jan. 2008

Firstpage

584

Lastpage

588

Abstract

The stochastic dependent-chance programming belongs to a class of stochastic programming problems, which has wide application backgrounds, in order to search an algorithm which can more effectively solve this problem, in the paper, stochastic simulation is used to produce training samples for BP neural networks, and a hybrid intelligent algorithm for stochastic dependent-chance programming combined PSO algorithm with BP neural networks for approximation of the chance function is presented. Finally, the simulation results of two examples are given to show the correctness and effectiveness of the algorithm.

Keywords

backpropagation; particle swarm optimisation; simulation; stochastic programming; BP neural networks; intelligent algorithm; particle swarm optimisation; stochastic dependent-chance programming; stochastic simulation; Approximation algorithms; Computational modeling; Computer simulation; Functional programming; Genetics; Intelligent networks; Neural networks; Space technology; Stochastic processes; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Discovery and Data Mining, 2008. WKDD 2008. First International Workshop on

Conference_Location

Adelaide, SA

Print_ISBN

978-0-7695-3090-1

Type

conf

DOI

10.1109/WKDD.2008.51

Filename

4470465