文章基本信息

标题：Complex Valued Recurrent Neural Network: From Architecture to Training
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作者：Alexey Minin ; Alois Knoll ; Hans-Georg Zimmermann 等
期刊名称：Journal of Signal and Information Processing
印刷版ISSN：2159-4465
电子版ISSN：2159-4481
出版年度：2012
卷号：3
期号：2
页码：192-197
DOI：10.4236/jsip.2012.32026
出版社：Scientific Research Publishing
摘要：Recurrent Neural Networks were invented a long time ago, and dozens of different architectures have been published. In this paper we generalize recurrent architectures to a state space model, and we also generalize the numbers the network can process to the complex domain. We show how to train the recurrent network in the complex valued case, and we present the theorems and procedures to make the training stable. We also show that the complex valued recurrent neural network is a generalization of the real valued counterpart and that it has specific advantages over the latter. We conclude the paper with a discussion of possible applications and scenarios for using these networks.
关键词：Complex Valued Neural Networks; Complex Valued System Identification; Recurrent Neural Networks; Complex Valued Recurrent Neural Networks