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Switching event-triggered control for mean-square exponential synchronization of complex-valued delayed memristive neural networks under hybrid attacks

Author

Listed:
  • Xiao, Shuai
  • Wang, Zhen
  • Wang, Hongjue
  • Liu, Yueying
  • Huang, Xia

Abstract

This paper investigates the mean-square exponential synchronization (MSES) of complex-valued delayed memristive neural networks (CVDMNNs) under complex-valued hybrid attacks. Considering the discontinuous right-hand sides of CVDMNNs, the systems are analyzed based on Filippov differential inclusion theory, and the interval matrix method is used to handle the memristive connection weights, thereby constructing error systems that are amenable to analysis. To utilize limited communication resources, a complex-valued switching event-triggered mechanism is introduced. Furthermore, to mitigate the effects of complex-valued hybrid attacks, consisting of complex-valued replay attacks and deception attacks following the Bernoulli distribution, a secure controller is designed that can simultaneously counteract the interference caused by residual terms in the error systems. On this basis, a piecewise Lyapunov functional is constructed, and the sufficient condition for achieving MSES under complex-valued hybrid attacks is derived using Lyapunov stability theory and inequality techniques. Based on the derived synchronization criterion, algorithms are proposed to determine the maximum allowable replay and deception attack rates of the system. Finally, a numerical example is provided to validate the effectiveness and superiority of the proposed scheme.

Suggested Citation

  • Xiao, Shuai & Wang, Zhen & Wang, Hongjue & Liu, Yueying & Huang, Xia, 2026. "Switching event-triggered control for mean-square exponential synchronization of complex-valued delayed memristive neural networks under hybrid attacks," Applied Mathematics and Computation, Elsevier, vol. 510(C).
    • Handle: RePEc:eee:apmaco:v:510:y:2026:i:c:s0096300325004412
    DOI: 10.1016/j.amc.2025.129715
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    References listed on IDEAS

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    1. Tuo Shi & Lili Gao & Yang Tian & Shuangzhu Tang & Jinchang Liu & Yiqi Li & Ruixi Zhou & Shiyu Cui & Hui Zhang & Yu Li & Zuheng Wu & Xumeng Zhang & Taihao Li & Xiaobing Yan & Qi Liu, 2025. "Memristor-based feature learning for pattern classification," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    2. Wu, Yuyan & Cheng, Jun & Zhou, Xia & Cao, Jinde & Luo, Mengzhuo, 2021. "Asynchronous filtering for nonhomogeneous Markov jumping systems with deception attacks," Applied Mathematics and Computation, Elsevier, vol. 394(C).
    3. Yang, Jinrong & Chen, Guici & Wen, Shiping & Wang, Leimin, 2023. "Finite-time dissipative control for discrete-time memristive neural networks via interval matrix method," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    4. Li, Xin & Lei, Anzhi & Zhu, Liangkuan & Ban, Mingfei, 2024. "Improving Kalman filter for cyber physical systems subject to replay attacks: An attack-detection-based compensation strategy," Applied Mathematics and Computation, Elsevier, vol. 466(C).
    5. Li, Ruoxia & Cao, Jinde, 2016. "Stability analysis of reaction-diffusion uncertain memristive neural networks with time-varying delays and leakage term," Applied Mathematics and Computation, Elsevier, vol. 278(C), pages 54-69.
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