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Almost sure exponential synchronization of drive-response stochastic memristive neural networks

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  • Chen, Siya
  • Feng, Jianwen
  • Wang, Jingyi
  • Zhao, Yi

Abstract

This paper concerns with the almost sure exponential synchronization for some general classes of drive-response stochastic memristive neural networks (SMNNs) with nonidentical nodes under state feedback controllers. The SMNNs considered may include networks which are asymmetrically nondelayed and delayed coupled simultaneously, and state-dependent or even those that are subject to exogenous stochastic perturbations representatively. The main results of this paper are a collection of generic sufficient conditions for guaranteed almost sure exponential synchronization of these SMNNs, which performs great advantages compared with mean-square synchronization. Furthermore, some practical corollaries are also obtained from the main results that may be directly applied to some smaller subclasses of these networks. In particular, a simpler and more effective way of almost surely exponentially synchronizing SMNNs without delays follows by considering them as a special case of SMNNs with delays. Some numerical simulations are given to illustrate our main theoretical findings.

Suggested Citation

  • Chen, Siya & Feng, Jianwen & Wang, Jingyi & Zhao, Yi, 2020. "Almost sure exponential synchronization of drive-response stochastic memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 383(C).
    • Handle: RePEc:eee:apmaco:v:383:y:2020:i:c:s0096300320303246
    DOI: 10.1016/j.amc.2020.125360
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    References listed on IDEAS

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    1. Dan Liu & Song Zhu & Wenting Chang, 2017. "Mean square exponential input-to-state stability of stochastic memristive complex-valued neural networks with time varying delay," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(9), pages 1966-1977, July.
    2. Li, Zhi-Min & Chang, Xiao-Heng & Yu, Lu, 2016. "Robust quantized H∞ filtering for discrete-time uncertain systems with packet dropouts," Applied Mathematics and Computation, Elsevier, vol. 275(C), pages 361-371.
    3. Song, Jia-Sheng & Chang, Xiao-Heng, 2020. "H∞ controller design of networked control systems with a new quantization structure," Applied Mathematics and Computation, Elsevier, vol. 376(C).
    4. Wu, Tianyu & Huang, Xia & Chen, Xiangyong & Wang, Jing, 2020. "Sampled-data H∞ exponential synchronization for delayed semi-Markov jump CDNs: A looped-functional approach," Applied Mathematics and Computation, Elsevier, vol. 377(C).
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    Cited by:

    1. Bao, Yuangui & Zhang, Yijun & Zhang, Baoyong, 2021. "Fixed-time synchronization of coupled memristive neural networks via event-triggered control," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    2. Fu, Qianhua & Zhong, Shouming & Shi, Kaibo, 2021. "Exponential synchronization of memristive neural networks with inertial and nonlinear coupling terms: Pinning impulsive control approaches," Applied Mathematics and Computation, Elsevier, vol. 402(C).
    3. Xia, Xue & Bai, Jing & Li, Xiaohe & Wen, Guoguang, 2023. "Containment control for fractional order MASs with nonlinearity and time delay via pull-based event-triggered mechanism," Applied Mathematics and Computation, Elsevier, vol. 454(C).
    4. Lin, Dongyuan & Chen, Xiaofeng & Yu, Guoping & Li, Zhongshan & Xia, Yannan, 2021. "Global exponential synchronization via nonlinear feedback control for delayed inertial memristor-based quaternion-valued neural networks with impulses," Applied Mathematics and Computation, Elsevier, vol. 401(C).

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