A real-coded genetic algorithm for training recurrent neural networks

被引：200

作者：

Blanco, A ^{[1
]}

Delgado, M ^{[1
]}

Pegalajar, MC ^{[1
]}

机构：

[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, ETSI Informat, E-18071 Granada, Spain

来源：

NEURAL NETWORKS | 2001年 / 14卷 / 01期

关键词：

recurrent neural network; fuzzy recurrent neural network; training algorithms; real-coded genetic algorithm; fuzzy grammatical inference; fuzzy finite-state automaton;

D O I：

10.1016/S0893-6080(00)00081-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a Real-Coded Genetic Algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our Genetic Algorithm with the Real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference. (C) 2001 Elsevier Science Ltd. All rights reserved.

引用

页码：93 / 105

页数：13

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