Anti-periodic solution for impulsive BAM neural networks with time-varying leakage delays on time scales

In this paper, by using a continuation theorem of coincidence degree theory and differential inequality techniques, we establish some sufficient conditions ensuring the existence and global exponential stability of anti-periodic solutions for a class of BAM neural networks with time-varying leakage...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 149; pp. 536 - 545
Main Authors Li, Yongkun, Yang, Li, Wu, Wanqin
Format Journal Article
LanguageEnglish
Published Elsevier B.V 03.02.2015
Subjects
Anti-periodic solutions
BAM neural networks
Coincidence degree
Leakage delays
Time scales
BAM neural networks
Leakage delays
Time scales
Anti-periodic solutions
Coincidence degree
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Summary:In this paper, by using a continuation theorem of coincidence degree theory and differential inequality techniques, we establish some sufficient conditions ensuring the existence and global exponential stability of anti-periodic solutions for a class of BAM neural networks with time-varying leakage delays and impulses on time scales. In addition, we present an illustrative example to show the feasibility of obtained results.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2014.08.020