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Delay-dependent attractor analysis of Hopfield neural networks with time-varying delays

Author

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  • Wan, Li
  • Zhou, Qinghua
  • Liu, Jie

Abstract

This paper investigates the attractor of Hopfield neural networks with time-varying delays. By using Lyapunov–Krasovskii functional as well as linear matrix inequality, some novel delay-dependent sufficient conditions are derived to ensure the existence of pullback attractor of the considered networks. The constraint that the derivative function of the delay function is less than 1 is removed. Finally, two examples are given to demonstrate the effectiveness of our theoretical result.

Suggested Citation

  • Wan, Li & Zhou, Qinghua & Liu, Jie, 2017. "Delay-dependent attractor analysis of Hopfield neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 101(C), pages 68-72.
    • Handle: RePEc:eee:chsofr:v:101:y:2017:i:c:p:68-72
    DOI: 10.1016/j.chaos.2017.05.017
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    References listed on IDEAS

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    1. Kaslik, E. & Balint, St., 2009. "Bifurcation analysis for a discrete-time Hopfield neural network of two neurons with two delays and self-connections," Chaos, Solitons & Fractals, Elsevier, vol. 39(1), pages 83-91.
    2. Liu, Linna & Zhu, Quanxin, 2015. "Almost sure exponential stability of numerical solutions to stochastic delay Hopfield neural networks," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 698-712.
    3. Shuo Zhang & Yongguang Yu & Wei Hu, 2014. "Robust Stability Analysis of Fractional-Order Hopfield Neural Networks with Parameter Uncertainties," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-14, April.
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