Anti-periodic solutions for shunting inhibitory cellular neural networks with time-varying delays in leakage terms

This paper is concerned with the existence and exponential stability of anti-periodic solutions for shunting inhibitory cellular neural networks (SICNNs) with time-varying delays in the leakage terms. Some sufficient conditions are established to ensure that all solutions of the networks converge ex...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 111; pp. 27 - 33
Main Authors Peng, Lequn, Wang, Wentao
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 02.07.2013
Elsevier
Subjects
Anti-periodic solution
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Connectionism. Neural networks
Exact sciences and technology
Exponential stability
Leakage term
Shunting inhibitory cellular neural networks
Time-varying delay
Shunting inhibitory cellular neural networks
Anti-periodic solution
Exponential stability
Leakage term
Time-varying delay
Cellular neural nets
Leak
Delay system
Neural network
Periodic solution
Modeling
Time varying system
Delay time
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Summary:This paper is concerned with the existence and exponential stability of anti-periodic solutions for shunting inhibitory cellular neural networks (SICNNs) with time-varying delays in the leakage terms. Some sufficient conditions are established to ensure that all solutions of the networks converge exponentially to the anti-periodic solution, which are new and complement of previously known results.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2012.11.031