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Sampled-data exponential synchronization of time-delay neural networks subject to random controller gain perturbations

Author

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  • Liu, Yamin
  • Xuan, Zuxing
  • Wang, Zhen
  • Zhou, Jianping
  • Liu, Yajuan

Abstract

In this paper, a non-fragile sampled-data control method is used to investigate the exponential synchronization of neural networks with discrete and distributed delays. The occurrence of controller gain perturbations is assumed to be random, which is described by a stochastic variable with the Bernoulli distribution. An extended two-sided looped Lyapunov functional is constructed, which efficiently utilizes available state information of the sampled instants. By using the two-sided looped Lyapunov functional and introducing suitable free weighting matrices, a sufficient condition is derived under which the resulting synchronization-error system is exponentially stable. Then, a design scheme of the non-fragile sampled-data controller is proposed with the aid of some decoupling techniques. At last, a numerical example is provided to illustrate the effectiveness and superiority of the proposed sampled-data control method.

Suggested Citation

  • Liu, Yamin & Xuan, Zuxing & Wang, Zhen & Zhou, Jianping & Liu, Yajuan, 2020. "Sampled-data exponential synchronization of time-delay neural networks subject to random controller gain perturbations," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    • Handle: RePEc:eee:apmaco:v:385:y:2020:i:c:s0096300320303908
    DOI: 10.1016/j.amc.2020.125429
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    References listed on IDEAS

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    6. Tai, Weipeng & Teng, Qingyong & Zhou, Youmei & Zhou, Jianping & Wang, Zhen, 2019. "Chaos synchronization of stochastic reaction-diffusion time-delay neural networks via non-fragile output-feedback control," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 115-127.
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    Cited by:

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    2. Ganesan, Bhuvaneshwari & Annamalai, Manivannan, 2023. "Anti-synchronization analysis of chaotic neural networks using delay product type looped-Lyapunov functional," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    3. Yan, Zhilian & Guo, Tong & Zhao, Anqi & Kong, Qingkai & Zhou, Jianping, 2022. "Reliable exponential H∞ filtering for a class of switched reaction-diffusion neural networks," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    4. Nguyen, Khanh Hieu & Kim, Sung Hyun, 2022. "Improved sampled-data control design of T-S fuzzy systems against mismatched fuzzy-basis functions," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    5. Cao, Yang & Udhayakumar, K. & Veerakumari, K. Pradeepa & Rakkiyappan, R., 2022. "Memory sampled data control for switched-type neural networks and its application in image secure communications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 201(C), pages 564-587.

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