PSO-BP Combined Artificial Neural Network Method Research

被引：0

作者：

Liu, Guiling ^{[1
]}

Gao, Feng ^{[1
]}

机构：

[1] Shanxi Datong Univ, Coal Engn Sch, Datong 037003, Shanxi Province, Peoples R China

来源：

ADVANCES IN CIVIL AND INDUSTRIAL ENGINEERING, PTS 1-4 | 2013年 / 353-356卷

关键词：

ANN; BP algorithm; PSO algorithm; algorithm design;

D O I：

10.4028/www.scientific.net/AMM.353-356.3537

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

BP artificial neural network(ANN) based on gradient algorithm method is widely applied, but because the error surface of object function is very complex and the choose of initial value effects network training results, convergence rate is slow and local minimum is likely to fall into. Particle swarm optimization(PSO) algorithm has better global searching ability to get rid the puzzles of falling into local minimum. By adequately studying on the two algorithms' characteristics, a new type of combined ANN training method is put forward, and PSO-BP ann model is successfully built.

引用

页码：3537 / 3540

页数：4

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