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Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities

Author

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  • Li, Feng
  • Song, Shuai
  • Zhao, Jianrong
  • Xu, Shengyuan
  • Zhang, Zhengqiang

Abstract

This paper pays attention to the synchronization control issue for Markov jump neural networks with partial information on system modes (or called Markov states), which leads to the case that the system modes cannot be directly accessed. An hidden Markov model (HMM)-based detector with partially known detection probabilities is employed to detect the system modes. With the help of the HMM and an activation function dividing method, a less conservative controller design technique is established. The designed HMM-based controller can be converted to mode-independent/-dependent one by suitably adjusting some design parameters. Finally, the availability of the established HMM-based controller design technique is verified by an illustrative example.

Suggested Citation

  • Li, Feng & Song, Shuai & Zhao, Jianrong & Xu, Shengyuan & Zhang, Zhengqiang, 2019. "Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities," Applied Mathematics and Computation, Elsevier, vol. 360(C), pages 1-13.
    • Handle: RePEc:eee:apmaco:v:360:y:2019:i:c:p:1-13
    DOI: 10.1016/j.amc.2019.04.032
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    References listed on IDEAS

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    1. Wang, Jing & Liang, Kun & Huang, Xia & Wang, Zhen & Shen, Hao, 2018. "Dissipative fault-tolerant control for nonlinear singular perturbed systems with Markov jumping parameters based on slow state feedback," Applied Mathematics and Computation, Elsevier, vol. 328(C), pages 247-262.
    2. Jiao, Shiyu & Shen, Hao & Wei, Yunliang & Huang, Xia & Wang, Zhen, 2018. "Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 338-350.
    3. Kalpana, M. & Balasubramaniam, P. & Ratnavelu, K., 2015. "Direct delay decomposition approach to synchronization of chaotic fuzzy cellular neural networks with discrete, unbounded distributed delays and Markovian jumping parameters," Applied Mathematics and Computation, Elsevier, vol. 254(C), pages 291-304.
    4. Li, Feng & Shen, Hao & Chen, Mengshen & Kong, Qingkai, 2015. "Non-fragile finite-time l2−l∞ state estimation for discrete-time Markov jump neural networks with unreliable communication links," Applied Mathematics and Computation, Elsevier, vol. 271(C), pages 467-481.
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    Cited by:

    1. Guo, Beibei & Xiao, Yu, 2023. "Intermittent synchronization for multi-link and multi-delayed large-scale systems with semi-Markov jump and its application of Chua’s circuits," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Xie, Lifei & Cheng, Jun & Wang, Hailing & Wang, Jiange & Hu, Mengjie & Zhou, Zhidong, 2022. "Memory-based event-triggered asynchronous control for semi-Markov switching systems," Applied Mathematics and Computation, Elsevier, vol. 415(C).
    3. Wang, Xiaomin & Li, Feng & Hu, Xingliu & Wang, Jing, 2023. "Mixed H∞/passive synchronization for persistent dwell-time switched neural networks via an activation function dividing method," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    4. He, Hangfeng & Qi, Wenhai & Kao, Yonggui, 2021. "HMM-based adaptive attack-resilient control for Markov jump system and application to an aircraft model," Applied Mathematics and Computation, Elsevier, vol. 392(C).

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