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Synchronization of discrete-time recurrent neural networks with time-varying delays via quantized sliding mode control

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  • Sun, Bo
  • Cao, Yuting
  • Guo, Zhenyuan
  • Yan, Zheng
  • Wen, Shiping

Abstract

In this paper, we discuss synchronization of discrete-time recurrent neural networks (DRNNs) with time-varying delays via quantized sliding mode control. A feedback controller based on sliding mode control is firstly imported in the synchronization of DRNNs. The activation functional in our paper can be more relaxed than the other papers which should satisfy the Lipschitz conditions. For the sake of reducing the computational complexity and conservatism, we consider two quantized methods with uniform and logarithmic quantizer. We gain some specific conditions to ensure the synchronization of discrete-time system. Several examples are presented to support our theorem in the ending.

Suggested Citation

  • Sun, Bo & Cao, Yuting & Guo, Zhenyuan & Yan, Zheng & Wen, Shiping, 2020. "Synchronization of discrete-time recurrent neural networks with time-varying delays via quantized sliding mode control," Applied Mathematics and Computation, Elsevier, vol. 375(C).
    • Handle: RePEc:eee:apmaco:v:375:y:2020:i:c:s009630032030062x
    DOI: 10.1016/j.amc.2020.125093
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    References listed on IDEAS

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    1. Luo, Mengzhuo & Cheng, Jun & Liu, Xinzhi & Zhong, Shouming, 2019. "An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control," Applied Mathematics and Computation, Elsevier, vol. 344, pages 163-182.
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    4. Luo, Mengzhuo & Liu, Xinzhi & Zhong, Shouming & Cheng, Jun, 2018. "Synchronization of stochastic complex networks with discrete-time and distributed coupling delayed via hybrid nonlinear and impulsive control," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 381-393.
    5. A.G., Soriano–Sánchez & C., Posadas–Castillo & M.A., Platas–Garza & A., Arellano–Delgado, 2018. "Synchronization and FPGA realization of complex networks with fractional–order Liu chaotic oscillators," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 250-262.
    6. Wang, Shengbo & Cao, Yanyi & Huang, Tingwen & Wen, Shiping, 2019. "Passivity and passification of memristive neural networks with leakage term and time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 294-310.
    7. Luo, Mengzhuo & Liu, Xinzhi & Zhong, Shouming & Cheng, Jun, 2018. "Synchronization of multi-stochastic-link complex networks via aperiodically intermittent control with two different switched periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 20-38.
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    Cited by:

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    3. Wang, Yuxiao & Cao, Yuting & Guo, Zhenyuan & Huang, Tingwen & Wen, Shiping, 2020. "Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm," Applied Mathematics and Computation, Elsevier, vol. 383(C).
    4. Gao, Panqing & Ye, Renyu & Zhang, Hai & Stamova, Ivanka & Cao, Jinde, 2024. "Asymptotic stability and quantitative synchronization of fractional competitive neural networks with multiple restrictions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 338-353.
    5. Li, Yongkun & Wang, Xiaohui, 2021. "Almost periodic solutions in distribution of Clifford-valued stochastic recurrent neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    6. Deng, Jie & Li, Hong-Li & Cao, Jinde & Hu, Cheng & Jiang, Haijun, 2023. "State estimation for discrete-time fractional-order neural networks with time-varying delays and uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).

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