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Robust synchronization of uncertain delayed neural networks with packet dropout using sampled-data control

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Abstract

Previous research for synchronization of neural networks have obtained some good results, but there are still some shortcomings in the research of uncertain delayed neural networks (DNN) with packet dropout. In this paper, we investigate the robust synchronization problem for uncertain neural networks with time-delay and packet dropout under sampled-data control (SDC). A neoteric time-delay Lyapunov-Krasovskii functional (TDLKF) with discrete and distributed delays is constructed by introducing some new constraints. With the help of the constructed TDLKF and the improved integral inequality, some stability criteria are derived to guarantee the error system synchronize exponentially when package dropout occurs randomly. The corresponding sampled-data controller can be acquired by solving a host of linear matrix inequalities (LMIs). Some numerical examples are used to illustrate the validity of the proposed method. The results show that the proposed method is an effective control strategy to solve the synchronization control problem of uncertain delayed neural networks with packet dropout.

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Authors and Affiliations

  1. Institute of Artificial intelligence, North China University of Science and Technology, Tangshan, 063009, China

    Ganlei Zhang & Wei Li

  2. Institute of Mining Engineering, North China University of Science and Technology, Tangshan, 063009, China

    Jiayong Zhang

  3. Institute of Electrical Engineering, North China University of Science and Technology, Tangshan, 063009, China

    Chao Ge

  4. Control and Computer Engineering, North China Electric Power University, Beijing, 102200, China

    Yajuan Liu

Authors
  1. Ganlei Zhang
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  2. Jiayong Zhang
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  3. Wei Li
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  4. Chao Ge
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  5. Yajuan Liu
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Correspondence to Jiayong Zhang, Wei Li or Chao Ge.

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Zhang, G., Zhang, J., Li, W. et al. Robust synchronization of uncertain delayed neural networks with packet dropout using sampled-data control. Appl Intell 51, 9054–9065 (2021). https://doi.org/10.1007/s10489-021-02388-1

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