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Global asymptotical synchronization of chaotic neural networks by output feedback impulsive control: An LMI approach

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  • Lu, Jun Guo
  • Chen, Guanrong

Abstract

In this paper, impulsive control for master–slave synchronization schemes consisting of identical chaotic neural networks is studied. Impulsive control laws are derived based on linear static output feedback. A sufficient condition for global asymptotic synchronization of master–slave chaotic neural networks via output feedback impulsive control is established, in which synchronization is proven in terms of the synchronization errors between the full state vectors. An LMI-based approach for designing linear static output feedback impulsive control laws to globally asymptotically synchronize chaotic neural networks is discussed. With the help of LMI solvers, linear output feedback impulsive controllers can be easily obtained along with the bounds of the impulsive intervals for global asymptotic synchronization. The method is finally illustrated by numerical simulations.

Suggested Citation

  • Lu, Jun Guo & Chen, Guanrong, 2009. "Global asymptotical synchronization of chaotic neural networks by output feedback impulsive control: An LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2293-2300.
    • Handle: RePEc:eee:chsofr:v:41:y:2009:i:5:p:2293-2300
    DOI: 10.1016/j.chaos.2008.09.024
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    Cited by:

    1. Fang, Qingxiang & Peng, Jigen, 2018. "Synchronization of fractional-order linear complex networks with directed coupling topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 542-553.
    2. Yang, Xinsong & Huang, Chuangxia & Zhu, Quanxin, 2011. "Synchronization of switched neural networks with mixed delays via impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 44(10), pages 817-826.
    3. Luo, Tianjiao, 2019. "Stabilization of multi-group models with multiple dispersal and stochastic perturbation via feedback control based on discrete-time state observations," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 396-410.

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