期刊
INFORMATION SCIENCES
卷 129, 期 1-4, 页码 45-59出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/S0020-0255(00)00068-2
关键词
neural network training; backpropagation; epoch; genetic algorithms; global search algorithms; interpolation
In view of several limitations of gradient search techniques (e.g. backpropagation), global search techniques, including evolutionary programming and genetic algorithms (GAs), have been proposed for training neural networks (NNs). However, the effectiveness, ease-of-use, and efficiency of these global search techniques have not been compared extensively with gradient search techniques. Using five chaotic time series functions, this paper empirically compares a genetic algorithm with backpropagation for training NNs. The chaotic series are interesting because of their similarity to economic and financial series found in financial markets. (C) 2000 Published by Elsevier Science Inc. All rights reserved.
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