Neural network optimization using genetic algorithms for speech recognition

被引：0
|
作者：
Mouria-Beji, Faryel [1 ]
机构：
[1] ENSI/LIA, Artificial Intelligence Group, BP, 275, Cite Mahragene, 1082 Tunis, Tunisia
来源：
International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications | 2002年 / 10卷 / 02期
关键词：
Backpropagation - Feedforward neural networks - Genetic algorithms - Optimization - Speech recognition - Transfer functions;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper describes the implementation of a family of genetic algorithms (GAs) to optimize the structure as well as the convergence rate and the global search capability of a multilayer perceptron (MLP). It reports on evolutionary programming (EP) experiments to evolve suitable parametrization of a given MLP architecture for speech recognition. Using an Arabic phoneme corpus, we found that evolved networks compare favorably with classic speech recognition methods.
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页码：69 / 74
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