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An improved stability criterion of neural networks with time-varying delays in the form of quadratic function using novel geometry-based conditions

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  • Lee, Tae H.
  • Park, Myeong Jin
  • Park, Ju H.

Abstract

In this paper, the stability problem of neural networks is addressed by considering time-varying delays. By proposing novel geometry-based negative conditions for the form of quadratic function and constructing new augmented Lyapunov-Krasovskii functionals, a novel stability criterion is derived. Finally, to show the effectiveness of the proposed criterion, several numerical examples are given.

Suggested Citation

  • Lee, Tae H. & Park, Myeong Jin & Park, Ju H., 2021. "An improved stability criterion of neural networks with time-varying delays in the form of quadratic function using novel geometry-based conditions," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    • Handle: RePEc:eee:apmaco:v:404:y:2021:i:c:s0096300321003167
    DOI: 10.1016/j.amc.2021.126226
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    References listed on IDEAS

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    1. Zhang, Guodong & Zeng, Zhigang, 2018. "Exponential stability for a class of memristive neural networks with mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 544-554.
    2. Zhang, Chuan-Ke & He, Yong & Jiang, Lin & Lin, Wen-Juan & Wu, Min, 2017. "Delay-dependent stability analysis of neural networks with time-varying delay: A generalized free-weighting-matrix approach," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 102-120.
    3. de Oliveira, Fúlvia S.S. & Souza, Fernando O., 2020. "Further refinements in stability conditions for time-varying delay systems," Applied Mathematics and Computation, Elsevier, vol. 369(C).
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