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Matrix measure strategies for exponential synchronization and anti-synchronization of memristor-based neural networks with time-varying delays

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  • Bao, Haibo
  • Park, Ju H.
  • Cao, Jinde

Abstract

This paper is concerned with exponential synchronization and anti-synchronization of memristor-based neural networks. Under the framework of Filippov systems and a linear controller, the exponential synchronization and anti-synchronization criteria for memristor-based neural networks can be guaranteed by the matrix measure and Halanay inequality. The criteria are very simple to implement in practice. Finally, two numerical examples are given to demonstrate the correctness of the theoretical results. It is shown that the matrix measure can increase the exponential convergence rate and decrease the feedback gain effectively.

Suggested Citation

  • Bao, Haibo & Park, Ju H. & Cao, Jinde, 2015. "Matrix measure strategies for exponential synchronization and anti-synchronization of memristor-based neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 543-556.
    • Handle: RePEc:eee:apmaco:v:270:y:2015:i:c:p:543-556
    DOI: 10.1016/j.amc.2015.08.064
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    References listed on IDEAS

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    1. Park, Ju H., 2009. "Synchronization of cellular neural networks of neutral type via dynamic feedback controller," Chaos, Solitons & Fractals, Elsevier, vol. 42(3), pages 1299-1304.
    2. Mathiyalagan, K. & Park, Ju H. & Sakthivel, R., 2015. "Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 967-979.
    3. James M. Tour & Tao He, 2008. "The fourth element," Nature, Nature, vol. 453(7191), pages 42-43, May.
    4. Dmitri B. Strukov & Gregory S. Snider & Duncan R. Stewart & R. Stanley Williams, 2008. "The missing memristor found," Nature, Nature, vol. 453(7191), pages 80-83, May.
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    Cited by:

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    18. Shi, Yanchao & Cao, Jinde & Chen, Guanrong, 2017. "Exponential stability of complex-valued memristor-based neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 222-234.
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