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Delay-independent exponential stability of stochastic Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms

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Abstract

Different from the approaches used in the earlier papers, in this paper, the Halanay inequality technique, in combination with the Lyapunov method, is exploited to establish a delay-independent sufficient condition for the exponential stability of stochastic Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms. Moreover, for the deterministic delayed Cohen–Grossberg neural networks, with or without reaction–diffusion terms, sufficient criteria for their global exponential stability are also obtained. The proposed results improve and extend those in the earlier literature and are easier to verify. An example is also given to illustrate the correctness of our results.

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Authors and Affiliations

  1. Department of Mathematics, Southeast University, Nanjing, 210096, P.R. China

    Xiaolin Li & Jinde Cao

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  1. Xiaolin Li
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  2. Jinde Cao
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Correspondence to Jinde Cao.

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Li, X., Cao, J. Delay-independent exponential stability of stochastic Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms. Nonlinear Dyn 50, 363–371 (2007). https://doi.org/10.1007/s11071-006-9164-3

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  • DOI: https://doi.org/10.1007/s11071-006-9164-3

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