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Extended dissipative conditions for memristive neural networks with multiple time delays

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  • Xiao, Jianying
  • Zhong, Shouming

Abstract

This paper addresses the problem of extended dissipative conditions for memristive neural networks with multiple time delays. The multiple time delays contain discrete, distributed and leakage time-varying delays. Based on both nonsmooth analysis and Lyapunov method, the extended dissipative conditions are obtained by mainly applying differential inclusions, set-valued maps and some new integral inequalities. The extended dissipative conditions can be applied in judging l2−l∞ performance, H∞ action, passive behavior and dissipative dynamics in a unified framework. Finally, a numerical example is provided to demonstrate the effectiveness and less conservatism of the proposed criteria.

Suggested Citation

  • Xiao, Jianying & Zhong, Shouming, 2018. "Extended dissipative conditions for memristive neural networks with multiple time delays," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 145-163.
    • Handle: RePEc:eee:apmaco:v:323:y:2018:i:c:p:145-163
    DOI: 10.1016/j.amc.2017.11.053
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    References listed on IDEAS

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    1. Chen, Xiangyong & Park, Ju H. & Cao, Jinde & Qiu, Jianlong, 2017. "Sliding mode synchronization of multiple chaotic systems with uncertainties and disturbances," Applied Mathematics and Computation, Elsevier, vol. 308(C), pages 161-173.
    2. Bao, Haibo & Park, Ju H. & Cao, Jinde, 2015. "Matrix measure strategies for exponential synchronization and anti-synchronization of memristor-based neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 543-556.
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    Cited by:

    1. Chang, Wenting & Zhu, Song & Li, Jinyu & Sun, Kaili, 2018. "Global Mittag–Leffler stabilization of fractional-order complex-valued memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 346-362.
    2. Zeng, Deqiang & Zhang, Ruimei & Liu, Xinzhi & Zhong, Shouming & Shi, Kaibo, 2018. "Pinning stochastic sampled-data control for exponential synchronization of directed complex dynamical networks with sampled-data communications," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 102-118.
    3. Dong, Shiyu & Zhu, Hong & Zhong, Shouming & Shi, Kaibo & Liu, Yajuan, 2021. "New study on fixed-time synchronization control of delayed inertial memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 399(C).
    4. Zeng, Deqiang & Pu, Zhilin & Zhang, Ruimei & Zhong, Shouming & Liu, Yajuan & Wu, Guo-Cheng, 2019. "Stochastic reliable synchronization for coupled Markovian reaction–diffusion neural networks with actuator failures and generalized switching policies," Applied Mathematics and Computation, Elsevier, vol. 357(C), pages 88-106.

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