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Quasi-synchronization of multi-layer delayed neural networks with parameter mismatches via impulsive control

Author

Listed:
  • Shi, Lingna
  • Li, Jiarong
  • Jiang, Haijun
  • Wang, Jinling

Abstract

This paper is concerned with the quasi-synchronization of delayed neural networks with multi-layer network structure and parameter mismatches. The topologies of different layers are independent of each other, and the individual dynamics of different nodes are different as well. Noting that uncertain parameters are taken into account as it is inevitable during the implementation of synchronization. In order to save the control cost, impulsive control that only controls network nodes at discrete times is proposed. Through the impulsive comparison principle, the quasi-synchronization criteria are established under the synchronizing impulses and desynchronizing impulses, respectively. In addition, the theoretical error bounds are estimated. The effectiveness of the theoretical results are verified by numerical simulation.

Suggested Citation

  • Shi, Lingna & Li, Jiarong & Jiang, Haijun & Wang, Jinling, 2023. "Quasi-synchronization of multi-layer delayed neural networks with parameter mismatches via impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    • Handle: RePEc:eee:chsofr:v:175:y:2023:i:p1:s0960077923008950
    DOI: 10.1016/j.chaos.2023.113994
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    References listed on IDEAS

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    1. Cai, Shuiming & Hou, Meiyuan, 2021. "Quasi-synchronization of fractional-order heterogeneous dynamical networks via aperiodic intermittent pinning control," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    2. Zhu, Ruiyuan & Guo, Yingxin & Wang, Fei, 2020. "Quasi-synchronization of heterogeneous neural networks with distributed and proportional delays via impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    3. Yan, Hongyun & Qiao, Yuanhua & Duan, Lijuan & Miao, Jun, 2022. "New results of quasi-projective synchronization for fractional-order complex-valued neural networks with leakage and discrete delays," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    4. Wang, Yan & Cheng, Wei & Feng, Junbo & Zang, Shengyin & Cheng, Hao & Peng, Zheng & Ren, Xiaodong & Shuai, Yubei & Liu, Hao & Pu, Xun & Yang, Junbo & Wu, Jiagui, 2022. "Silicon photonic secure communication using artificial neural network," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    5. Kumar, Ankit & Das, Subir & Singh, Sunny & Rajeev,, 2023. "Quasi-projective synchronization of inertial complex-valued recurrent neural networks with mixed time-varying delay and mismatched parameters," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    6. Kumar, Ankit & Das, Subir & Yadav, Vijay K. & Rajeev,, 2021. "Global quasi-synchronization of complex-valued recurrent neural networks with time-varying delay and interaction terms," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    7. Zhang, Lingzhong & Lu, Jianquan & Jiang, Bangxin & Huang, Chi, 2023. "Distributed synchronization of delayed dynamic networks under asynchronous delay-dependent impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    8. Hu, Qinghua & Zhang, Rujia & Zhou, Yucan, 2016. "Transfer learning for short-term wind speed prediction with deep neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 83-95.
    9. Xiang, Jianglian & Ren, Junwu & Tan, Manchun, 2022. "Stability analysis for memristor-based stochastic multi-layer neural networks with coupling disturbance," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
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