Training Method for a Feed Forward Neural Network Based on Meta-heuristics

被引：0

作者：

Melo, Haydee ^{[1
]}

Zhang, Huiming ^{[1
]}

Vasant, Pandian ^{[2
]}

Watada, Junzo ^{[3
]}

机构：

[1] Waseda Univ, Grad Sch Prod Informat & Syst, Tokyo, Japan

[2] Univ Teknol PETRONAS, Fundamental & Appl Sci Dept, Seri Iskandar, Malaysia

[3] Univ Teknol PETRONAS, Deparment Comp & Informat Sci, Seri Iskandar, Malaysia

来源：

ADVANCES IN INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING, PT II | 2018年 / 82卷

关键词：

Particle Swarm Optimization; Neural network; Training algorithm; Gaussian distribution; Cauchy distribution;

D O I：

10.1007/978-3-319-63859-1_46

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a Gaussian-Cauchy Particle Swarm Optimization (PSO) algorithm to provide the optimized parameters for a Feed Forward Neural Network. The improved PSO trains the Neural Network by optimizing the network weights and bias in the Neural Network. In comparison with the Back Propagation Neural Network, the Gaussian-Cauchy PSO Neural Network converges faster and is immune to local minima.

引用

页码：378 / 385

页数：8

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