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Convergence Analysis of Recurrent Neural Networks
     

Convergence Analysis of Recurrent Neural Networks

by Zhang Yi
 

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Yi (School of Computer Science and Engineering, University of Electronic Science and Technology, China) and Tan (Department of Electrical and Computer Engineering, The National University of Singapore) present recent original research results on different types of recurrent neural networks (RNNs), including Hopfield, cellular, and Lotka-Volterra RNNs, RNNs with

Overview

Yi (School of Computer Science and Engineering, University of Electronic Science and Technology, China) and Tan (Department of Electrical and Computer Engineering, The National University of Singapore) present recent original research results on different types of recurrent neural networks (RNNs), including Hopfield, cellular, and Lotka-Volterra RNNs, RNNs with unsaturating activation functions, and discrete time RNNs. Focus is on monostability and multistability convergence of RNNs. While the main objective of the book is to disseminate results, the book is also written to be helpful to readers requiring basic information. The book will be of interest to researchers and graduate students in neural computations and neural networks. There is no subject index. Annotation ©2004 Book News, Inc., Portland, OR

Product Details

ISBN-13:
9781475738216
Publisher:
Springer US
Publication date:
04/30/2014
Series:
Network Theory and Applications Series , #13
Edition description:
Softcover reprint of the original 1st ed. 2004
Pages:
233
Product dimensions:
6.10(w) x 9.25(h) x 0.02(d)

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