期刊
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING
卷 11, 期 3, 页码 357-363出版社
WILEY
DOI: 10.1002/tee.22225
关键词
complex-valued neural networks; associative memory; noise tolerance; learning algorithm
Complex-valued associative memories (CAMs) are one of the most promising associative memory models by neural networks. However, the low noise tolerance of CAMs is often a serious problem. A projection learning rule with large constant terms improves the noise tolerance of CAMs. However, the projection learning rule can be applied only to CAMs with full connections. In this paper, we propose a gradient descent learning rule with large constant terms, which is not restricted by network topology. We realize large constant terms by regularization to connection weights. By computer simulations, we prove that the proposed learning algorithm improves noise tolerance. (c) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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