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Globally Exponential Stability of Periodic Solutions to Impulsive Neural Networks with Time-Varying Delays

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  • Yuanfu Shao
  • Changjin Xu
  • Qianhong Zhang

Abstract

By using Schaeffer's theorem and Lyapunov functional, sufficient conditions of the existence and globally exponential stability of positive periodic solution to an impulsive neural network with time-varying delays are established. Applications, examples, and numerical analysis are given to illustrate the effectiveness of the main results.

Suggested Citation

  • Yuanfu Shao & Changjin Xu & Qianhong Zhang, 2012. "Globally Exponential Stability of Periodic Solutions to Impulsive Neural Networks with Time-Varying Delays," Abstract and Applied Analysis, Hindawi, vol. 2012, pages 1-14, May.
  • Handle: RePEc:hin:jnlaaa:358362
    DOI: 10.1155/2012/358362
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    Cited by:

    1. Suo, JingJing & Hu, Hongxiao & Xu, Liguang, 2023. "Delay-dependent impulsive control for lag quasi-synchronization of stochastic complex dynamical networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 211(C), pages 134-153.
    2. Xu, Changjin & Li, Peiluan, 2017. "Global exponential convergence of neutral-type Hopfield neural networks with multi-proportional delays and leakage delays," Chaos, Solitons & Fractals, Elsevier, vol. 96(C), pages 139-144.
    3. Gani Stamov & Ivanka Stamova & Stanislav Simeonov & Ivan Torlakov, 2020. "On the Stability with Respect to H-Manifolds for Cohen–Grossberg-Type Bidirectional Associative Memory Neural Networks with Variable Impulsive Perturbations and Time-Varying Delays," Mathematics, MDPI, vol. 8(3), pages 1-14, March.

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