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Stochastic reliable synchronization for coupled Markovian reaction–diffusion neural networks with actuator failures and generalized switching policies

Author

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  • Zeng, Deqiang
  • Pu, Zhilin
  • Zhang, Ruimei
  • Zhong, Shouming
  • Liu, Yajuan
  • Wu, Guo-Cheng

Abstract

This paper is concerned with the synchronization of coupled Markovian reaction–diffusion neural networks (RDNNs) with actuator failures and generalized switching policies (GSPs) via mode-dependent reliable control. Different from some existing results with all known transition rates, GSPs are considered for coupled Markovian RDNNs, where each transition rate can be completely unknown or only its estimation is known, or each transition rate of some modes is completely unknown. To reflect more realistic behaviors, actuator failures are considered for coupled Markovian RDNNs, and a mode-dependent reliable control scheme is proposed. Then, a new Lyapunov–Krasovskii functional (LKF) is introduced, which fully utilizes the information on the slope of neuron activation functions. Based on the LKF, a synchronization criterion is established in the form of linear matrix inequalities (LMIs). Moreover, the mode-dependent reliable control gains are obtained. Finally, a numerical example is given to verify the effectiveness of the proposed results.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:apmaco:v:357:y:2019:i:c:p:88-106
    DOI: 10.1016/j.amc.2019.03.055
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    References listed on IDEAS

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    1. Wang, Jing & Hu, Xiaohui & Wei, Yunliang & Wang, Zhen, 2019. "Sampled-data synchronization of semi-Markov jump complex dynamical networks subject to generalized dissipativity property," Applied Mathematics and Computation, Elsevier, vol. 346(C), pages 853-864.
    2. Jianping Zhou & Shengyuan Xu & Hao Shen & Baoyong Zhang, 2013. "Passivity analysis for uncertain BAM neural networks with time delays and reaction–diffusions," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(8), pages 1494-1503.
    3. R. Saravanakumar & M. Syed Ali & H. R. Karimi, 2017. "Robust control of uncertain stochastic Markovian jump systems with mixed time-varying delays," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(4), pages 862-872, March.
    4. Zhou, Jianping & Sang, Chengyan & Li, Xiao & Fang, Muyun & Wang, Zhen, 2018. "H∞ consensus for nonlinear stochastic multi-agent systems with time delay," Applied Mathematics and Computation, Elsevier, vol. 325(C), pages 41-58.
    5. 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.
    6. 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.
    7. 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.
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    Cited by:

    1. Wu, Tao & Cao, Jinde & Xiong, Lianglin & Zhang, Haiyang & Shu, Jinlong, 2022. "Sampled-data synchronization criteria for Markovian jumping neural networks with additive time-varying delays using new techniques," Applied Mathematics and Computation, Elsevier, vol. 413(C).
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    3. Nguyen, Khanh Hieu & Kim, Sung Hyun, 2020. "Observer-based control design of semi-Markovian jump systems with uncertain probability intensities and mode-transition-dependent sojourn-time distribution," Applied Mathematics and Computation, Elsevier, vol. 372(C).

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