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An ℋ∞ approach to stability analysis of switched Hopfield neural networks with time-delay

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Abstract

This paper proposes a new ℋ∞ weight learning law for switched Hopfield neural networks with time-delay under parametric uncertainty. For the first time, the ℋ∞ weight learning law is presented to not only guarantee the asymptotical stability of switched Hopfield neural networks, but also reduce the effect of external disturbance to an ℋ∞ norm constraint. An existence condition for the ℋ∞ weight learning law of switched Hopfield neural networks is expressed in terms of strict linear matrix inequality (LMI). Finally, a numerical example is provided to illustrate our results.

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Authors and Affiliations

  1. Division of Electronics and Control Engineering, Wonkwang University, Iksan, Korea

    Choon Ki Ahn

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  1. Choon Ki Ahn
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Correspondence to Choon Ki Ahn.

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This paper was supported by Wonkwang University in 2010.

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Ahn, C.K. An ℋ∞ approach to stability analysis of switched Hopfield neural networks with time-delay. Nonlinear Dyn 60, 703–711 (2010). https://doi.org/10.1007/s11071-009-9625-6

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  • DOI: https://doi.org/10.1007/s11071-009-9625-6

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