Grammatical Swarm for Artificial Neural Network Training

被引：0
|
作者：
Si, Tapas [1 ]
De, Arunava [2 ]
Bhattacharjee, Anup Kumar [3 ]
机构：
[1] Bankura Unnayani Inst Engn, Dept CSE, Bankura, WB, India
[2] Dr BC Roy Engn Coll, Dept IT, Durgapur, W Bengal, India
[3] Natl Inst Technol, Dept ECE, Durgapur, W Bengal, India
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON CIRCUIT, POWER AND COMPUTING TECHNOLOGIES (ICCPCT-2014) | 2014年
关键词：
Artificial neural network; Grammatical evolution; Grammatical swarm; Particle swarm optimizer; Comprehensive learning particle swarm optimizer; Differential evolution; Trigonometric differential evolution; XOR problem; EVOLUTION;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper presents a proof of concept for Artificial Neural Network training using Grammatical Swarm. Grammatical Swarm is a variant of Grammatical Evolution. The synaptic weight coefficients of a multilayer feed-forward neural network are evolved using Grammatical Swarm. The synaptic weight coefficients are derived from predefined BackusNaur Form grammar for real value generation in a specified range. The proposed method is applied to solve XOR problem and compared with the multilayer feed-forward neural network training using Particle Swarm Optimizer, Comprehensive Learning Particle Swarm Optimizer, Differential Evolution and Trigonometric Differential Evolution. The experimental results shows that Grammatical Swarm is able to train the Artificial Neural Network.
引用
收藏
页码：1657 / 1661
页数：5
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