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Exponential synchronization of chaotic neural networks with time-varying delay via intermittent output feedback approach

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  • Zhang, Zhi-Ming
  • He, Yong
  • Wu, Min
  • Wang, Qing-Guo

Abstract

This paper is dealt with the problem of exponential synchronization for chaotic neural networks with time-varying delay by using intermittent output feedback control. Based on the Lyapunov–Krasovskii functional method and the lower bound lemma for reciprocally convex technique, a novel criterion for existence of the controller is first established to ensure synchronization between the master and slave systems. Moreover, from the delay point of view, the derived criterion is extended to the relaxed case because of introducing an adjustable parameter in the Lyapunov–Krasovskii functional. Finally, a numerical simulation is carried out to demonstrate the effectiveness of the proposed synchronization law.

Suggested Citation

  • Zhang, Zhi-Ming & He, Yong & Wu, Min & Wang, Qing-Guo, 2017. "Exponential synchronization of chaotic neural networks with time-varying delay via intermittent output feedback approach," Applied Mathematics and Computation, Elsevier, vol. 314(C), pages 121-132.
    • Handle: RePEc:eee:apmaco:v:314:y:2017:i:c:p:121-132
    DOI: 10.1016/j.amc.2017.07.019
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. A.G., Soriano–Sánchez & C., Posadas–Castillo & M.A., Platas–Garza & A., Arellano–Delgado, 2018. "Synchronization and FPGA realization of complex networks with fractional–order Liu chaotic oscillators," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 250-262.
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    5. Li, Xiaoqing & She, Kun & Zhong, Shouming & Shi, Kaibo & Kang, Wei & Cheng, Jun & Yu, Yongbin, 2018. "Extended robust global exponential stability for uncertain switched memristor-based neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 325(C), pages 271-290.

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