BP neural network optimized with PSO algorithm and its application in forecasting

被引：13

作者：

Guo, Wen ^{[1
]}

Qiao, Yizheng ^{[1
]}

Hou, Haiyan ^{[1
]}

机构：

[1] Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Peoples R China

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON INFORMATION ACQUISITION, VOLS 1 AND 2, CONFERENCE PROCEEDINGS | 2006年

关键词：

BP neural network; training algorithm; particle swarm optimization; evolution computation; forecasting;

D O I：

10.1109/ICIA.2006.305796

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

An approach that neural network optimized with PSO algorithm is proposed in the paper. Unlike conventional training method with gradient descent method only, this paper introduces a hybrid training algorithm by combining the PSO and BP algorithm. The PSO is used to optimize the initial parameters of the BP neural network, including the weights and biases. It can effectively better the cases that network is easily trapped to a local optimum and has a slow velocity of convergence. The experiment results show the method in the paper above conventional one has greater improvement in both accuracy and velocity of convergence for BP neural network.

引用

页码：617 / 621

页数：5

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