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New stability results for delayed neural networks

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  • Shao, Hanyong
  • Li, Huanhuan
  • Zhu, Chuanjie

Abstract

This paper is concerned with the stability for delayed neural networks. By more fully making use of the information of the activation function, a new Lyapunov–Krasovskii functional (LKF) is constructed. Then a new integral inequality is developed, and more information of the activation function is taken into account when the derivative of the LKF is estimated. By Lyapunov stability theory, a new stability result is obtained. Finally, three examples are given to illustrate the stability result is less conservative than some recently reported ones.

Suggested Citation

  • Shao, Hanyong & Li, Huanhuan & Zhu, Chuanjie, 2017. "New stability results for delayed neural networks," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 324-334.
    • Handle: RePEc:eee:apmaco:v:311:y:2017:i:c:p:324-334
    DOI: 10.1016/j.amc.2017.05.023
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    References listed on IDEAS

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    1. Shao, Hanyong & Zhang, Zhengqiang, 2015. "Delay-dependent state feedback stabilization for a networked control model with two additive input delays," Applied Mathematics and Computation, Elsevier, vol. 265(C), pages 748-758.
    2. Ji, Meng-Di & He, Yong & Wu, Min & Zhang, Chuan-Ke, 2015. "Further results on exponential stability of neural networks with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 175-182.
    3. Raja, R. & Zhu, Quanxin & Senthilraj, S. & Samidurai, R., 2015. "Improved stability analysis of uncertain neutral type neural networks with leakage delays and impulsive effects," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 1050-1069.
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    Cited by:

    1. Wang, Lingyu & Huang, Tingwen & Xiao, Qiang, 2018. "Global exponential synchronization of nonautonomous recurrent neural networks with time delays on time scales," Applied Mathematics and Computation, Elsevier, vol. 328(C), pages 263-275.
    2. Yu, Siyi & Li, Hua & Chen, Xiaofeng & Lin, Dongyuan, 2023. "Multistability analysis of quaternion-valued neural networks with cosine activation functions," Applied Mathematics and Computation, Elsevier, vol. 445(C).
    3. 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.
    4. Feng, Zongying & Shao, Hanyong & Shao, Lin, 2020. "Further results on event-triggered H∞ networked control for neural networks with stochastic cyber-attacks," Applied Mathematics and Computation, Elsevier, vol. 386(C).
    5. Arunagirinathan, S. & Lee, T.H., 2024. "Generalized delay-dependent reciprocally convex inequality on stability for neural networks with time-varying delay," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 109-120.
    6. Liu, Yan & Mei, Jingling & Li, Wenxue, 2018. "Stochastic stabilization problem of complex networks without strong connectedness," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 304-315.

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