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Global exponential robust periodicity and stability of interval neural networks with both variable and unbounded delays

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  • Zhao, Zhenjiang

Abstract

By constructing proper vector Lyapunov functions and nonlinear integro-differential inequalities involving both variable delays and unbounded delays, and using M-matrix theory, several sufficient conditions are obtained. These conditions ensure the global exponential robust periodicity and stability of interval neural networks with both variable and unbounded delays. The assumptions on the boundedness of the activation functions and the differentiability of time-varying delays, needed in most other papers, are no longer necessary in the present study. The obtained results in this paper improve and extend those given in the earlier literature.

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  • Zhao, Zhenjiang, 2008. "Global exponential robust periodicity and stability of interval neural networks with both variable and unbounded delays," Chaos, Solitons & Fractals, Elsevier, vol. 36(1), pages 91-97.
    • Handle: RePEc:eee:chsofr:v:36:y:2008:i:1:p:91-97
    DOI: 10.1016/j.chaos.2006.06.011
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    Cited by:

    1. Hu, Jiming, 2009. "Synchronization conditions for chaotic nonlinear continuous neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2495-2501.

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