A hybrid artificial neural networks and particle swarm optimization for function approximation

被引：0

作者：

Su, Tejen ^{[1
]}

Jhang, Jyunwei ^{[1
]}

Hou, Chengchih ^{[1
]}

机构：

[1] Natl Kaohsiung Univ Appl Sci, Dept Elect Engn, Kaohsiung 807, Taiwan

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2008年 / 4卷 / 09期

关键词：

artificial neural networks; particle swarm optimization; function approximation; feedforward network;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the weights of the Artificial Neural Networks (ANN) are trained by Particle Swarm Optimization (PSO). Because PSO has the probabilistic mechanism, and multi-starting points, hence the PSO can avoid getting into the local optimal solutions. The demonstrated examples are presented to illustrate the better performance of the proposed methodology (PSO-ANN) than other existing methods.

引用

页码：2363 / 2374

页数：12

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