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Adaptive synchronization of Cohen–Grossberg neural network with mixed time-varying delays and stochastic perturbation

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  • Zhang, Chaolong
  • Deng, Feiqi
  • Peng, Yunjian
  • Zhang, Bo

Abstract

In this paper, based on the LaSalle invariant principle of stochastic differential delay equations and the stochastic analysis theory as well as the adaptive feedback technique, several sufficient conditions ensuring the adaptive synchronization of Cohen–Grossberg neural network with mixed time-varying delays and stochastic perturbation are derived. In particular, the synchronization criterion considered globally is the almost surely asymptotic stability of the error dynamical system. Our synchronization criterion is easily verified and does not solve any linear matrix inequality. These results generalized a few previous known results. At last, a numerical example and its simulations are provided to demonstrate the effectiveness and advantage of the theoretical results.

Suggested Citation

  • Zhang, Chaolong & Deng, Feiqi & Peng, Yunjian & Zhang, Bo, 2015. "Adaptive synchronization of Cohen–Grossberg neural network with mixed time-varying delays and stochastic perturbation," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 792-801.
    • Handle: RePEc:eee:apmaco:v:269:y:2015:i:c:p:792-801
    DOI: 10.1016/j.amc.2015.07.074
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    References listed on IDEAS

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    Cited by:

    1. Tu, Zhengwen & Ding, Nan & Li, Liangliang & Feng, Yuming & Zou, Limin & Zhang, Wei, 2017. "Adaptive synchronization of memristive neural networks with time-varying delays and reaction–diffusion term," Applied Mathematics and Computation, Elsevier, vol. 311(C), pages 118-128.
    2. 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.
    3. Jun Guo & Yanchao Shi & Weihua Luo & Yanzhao Cheng & Shengye Wang, 2023. "Adaptive Global Synchronization for a Class of Quaternion-Valued Cohen-Grossberg Neural Networks with Known or Unknown Parameters," Mathematics, MDPI, vol. 11(16), pages 1-16, August.
    4. Zhanying Yang & Jie Zhang, 2019. "Stability Analysis of Fractional-Order Bidirectional Associative Memory Neural Networks with Mixed Time-Varying Delays," Complexity, Hindawi, vol. 2019, pages 1-22, October.
    5. Li, Ruoxia & Cao, Jinde & Alsaedi, Ahmad & Alsaadi, Fuad, 2017. "Exponential and fixed-time synchronization of Cohen–Grossberg neural networks with time-varying delays and reaction-diffusion terms," Applied Mathematics and Computation, Elsevier, vol. 313(C), pages 37-51.
    6. Li, Xuemei & Liu, Xinge & Wang, Fengxian, 2023. "Anti-synchronization of fractional-order complex-valued neural networks with a leakage delay and time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    7. Zhao, Yong & Kurths, Jürgen & Duan, Lixia, 2018. "Input-to-State stability analysis for memristive Cohen-Grossberg-type neural networks with variable time delays," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 364-369.

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