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Noise-to-state practical stability and stabilization of random neural networks

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Abstract

This paper is devoted to studying noise-to-state practical stability and stabilization problems for random neural networks in the presence of general disturbances. It is proved that the existence and uniqueness of solutions is ensured if the noise intensity function is locally Lipschitz. Using random Lyapunov theory and the existence of practical Lyapunov functions, criteria are established for noise-to-state practical stability in mean of random neural networks. Some easily checkable and computable conditions are provided based on the structure characterization of the neural networks. Numerical examples are given to demonstrate the effectiveness of the developed methods.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, 255000, Shandong, People’s Republic of China

    Ticao Jiao

  2. School of Engineering, Qufu Normal University, Rizhao, 276826, Shandong, People’s Republic of China

    Guangdeng Zong

  3. School of Electrical Engineering, Korea University, Anam-Dong 5-Ga, Sungbuk-Gu, Seoul, 136-701, Republic of Korea

    C. K. Ahn

Authors
  1. Ticao Jiao
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  2. Guangdeng Zong
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  3. C. K. Ahn
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Correspondence to Ticao Jiao.

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This work is supported by National Natural Science Foundation of China (61703249), (61773235), (61673197), (61703132) and (51707110), a Project funded by China Postdoctoral Science Foundation (2019M652351) and Taishan Scholar Project of Shandong Province (TSQN20161033) .

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Jiao, T., Zong, G. & Ahn, C.K. Noise-to-state practical stability and stabilization of random neural networks. Nonlinear Dyn 100, 2469–2481 (2020). https://doi.org/10.1007/s11071-020-05628-0

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  • DOI: https://doi.org/10.1007/s11071-020-05628-0

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