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Hybrid event-triggered synchronization control of delayed chaotic neural networks against communication delay and random data loss

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  • Gao, Zifan
  • Zhang, Dawei
  • Zhu, Shuqian

Abstract

This paper investigates the network-based synchronization control of delayed chaotic neural networks. A new hybrid event-triggered communication scheme, which consists of fixed and dynamic triggering conditions, is presented to handle the random loss of triggered sampled-data. Under this scheme, a quantitative relation between the allowable data loss probability and the fixed triggering interval can be revealed. The synchronous error system is modeled by a multi-constrained system affected by nonlinear terms, piecewise input delay, stochastic update input and reset states. By using the boundary information and interrelation of both communication delay and input delay, a binary quadratic convex lemma and a tailored discontinuous augmented Lyapunov-Krasovskii functional approach are proposed to exploit delay-dependent synchronization criteria with less conservatism. Simulation results are provided for checking the merits of the obtained criteria.

Suggested Citation

  • Gao, Zifan & Zhang, Dawei & Zhu, Shuqian, 2023. "Hybrid event-triggered synchronization control of delayed chaotic neural networks against communication delay and random data loss," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:chsofr:v:172:y:2023:i:c:s0960077923004368
    DOI: 10.1016/j.chaos.2023.113535
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    References listed on IDEAS

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    1. Li, Xinna & Wu, Huaiqin & Cao, Jinde, 2023. "Prescribed-time synchronization in networks of piecewise smooth systems via a nonlinear dynamic event-triggered control strategy," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 647-668.
    2. Wang, Jun & Shi, Kaibo & Huang, Qinzhen & Zhong, Shouming & Zhang, Dian, 2018. "Stochastic switched sampled-data control for synchronization of delayed chaotic neural networks with packet dropout," Applied Mathematics and Computation, Elsevier, vol. 335(C), pages 211-230.
    3. Su, Haipeng & Luo, Runzi & Huang, Meichun & Fu, Jiaojiao, 2022. "Practical fixed time active control scheme for synchronization of a class of chaotic neural systems with external disturbances," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    4. Zhang, Ruimei & Zeng, Deqiang & Zhong, Shouming & Yu, Yongbin, 2017. "Event-triggered sampling control for stability and stabilization of memristive neural networks with communication delays," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 57-74.
    5. Huan, Mingchen & Li, Chuandong, 2023. "Synchronization of reaction–diffusion neural networks with sampled-data control via a new two-sided looped-functional," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    6. Zhang, Lingzhong & Lu, Jianquan & Jiang, Bangxin & Huang, Chi, 2023. "Distributed synchronization of delayed dynamic networks under asynchronous delay-dependent impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
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    Cited by:

    1. Karnan, A. & Nagamani, G., 2023. "Event-triggered extended dissipative synchronization for delayed neural networks with random uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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