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Global asymptotic stability analysis for integro-differential systems modeling neural networks with delays

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Abstract

In this paper, the global asymptotic stability of the equilibrium point of integro-differential systems modeling neural networks with time-varying delays are studied. Proper Lyapunov functionals and some analytic techniques are employed to derive the sufficient conditions under which the networks proposed are the global asymptotic stability. The results have shown to improve the previous global asymptotic stability results derived in the literature. Some examples are given to illustrate the correctness of our results.

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Authors and Affiliations

  1. Department of Mathematics, Qufu Normal University, Qufu, 273165, Shandong, China

    Yingxin Guo

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  1. Yingxin Guo
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Correspondence to Yingxin Guo.

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The author is supported financially by the NNSF of China (10801088).

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Guo, Y. Global asymptotic stability analysis for integro-differential systems modeling neural networks with delays. Z. Angew. Math. Phys. 61, 971–978 (2010). https://doi.org/10.1007/s00033-009-0057-4

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  • DOI: https://doi.org/10.1007/s00033-009-0057-4

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