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Global Exponential Stability and Periodicity of Recurrent Neural Networks With Time Delays.

Authors :
Jinde Cao
Jun Wang
Source :
IEEE Transactions on Circuits & Systems. Part I: Regular Papers; May2005, Vol. 52 Issue 5, p920-931, 12p
Publication Year :
2005

Abstract

In this paper, the global exponential stability and periodicity of a class of recurrent neural networks with time delays are addressed by using Lyapunov functional method and inequality techniques. The delayed neural network includes the well-known Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks as its special cases. New criteria are found to ascertain the global exponential stability and periodicity of the recurrent neural networks with time delays, and are also shown to be different from and improve upon existing ones. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15498328
Volume :
52
Issue :
5
Database :
Complementary Index
Journal :
IEEE Transactions on Circuits & Systems. Part I: Regular Papers
Publication Type :
Periodical
Accession number :
17227856
Full Text :
https://doi.org/10.1109/TCSI.2005.846211