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Quantized energy-to-peak state estimation for persistent dwell-time switched neural networks with packet dropouts

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Abstract

This paper pays close attention to the problem of energy-to-peak state estimation for a class of neural networks under switching mechanism. Persistent dwell-time switching rule, which is more generic than average dwell-time and dwell-time, is employed. In addition, the particular concept for persistent dwell-time, including the specific distinction between sample time and switching instant, is given. The measured output subject to quantized signals is used for alleviating the overhead about communication channel. At the same time, the random packet losses with its probability obeying Bernoulli distribution is considered. By the aid of a suitable mode-dependent Lyapunov function and switched system theory, the expected mode-dependent estimator is developed to guarantee that the resulting estimation error system is mean-square exponentially stable and meets a prescribed energy-to-peak performance index. In the end, the applicability of the proposed method is illustrated by utilizing a numerical example.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan, 243002, China

    Hao Shen & Zhengguo Huang

  2. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Zhen Wang

  3. School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang, 212003, China

    Xiaofei Yang

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  1. Hao Shen
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  4. Zhen Wang
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Correspondence to Zhen Wang.

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This work was supported by the National Natural Science Foundation of China under Grants 61304066,61573008,61473178,61703004, the Natural Science Foundation of Anhui Province under Grant 1708085MF165, the SDUST Research Fund under Grant 2014TDJH102.

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Shen, H., Huang, Z., Yang, X. et al. Quantized energy-to-peak state estimation for persistent dwell-time switched neural networks with packet dropouts. Nonlinear Dyn 93, 2249–2262 (2018). https://doi.org/10.1007/s11071-018-4322-y

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  • DOI: https://doi.org/10.1007/s11071-018-4322-y

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