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Finite-Time Projective Synchronization of Fractional-Order Memristive Neural Networks with Mixed Time-Varying Delays

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  • Meng Hui
  • Chen Wei
  • Jiao Zhang
  • Herbert Ho-Ching Iu
  • Ni Luo
  • Rui Yao
  • Lin Bai

Abstract

This paper is concerned with the finite-time projective synchronization problem of fractional-order memristive neural networks (FMNNs) with mixed time-varying delays. Firstly, under the frame of fractional-order differential inclusion and the set-valued map, several criteria are derived to ensure finite-time projective synchronization of FMNNs. Meanwhile, three properties are established to deal with different forms of the finite-time fractional differential inequation, which greatly extend some results on estimation of settling time of FMNNs. In addition to the traditional Lyapunov function with 1-norm form in Theorem 1, a more general and flexible Lyapunov function based on p-norm is constructed in Theorem 2 to analyze the finite-time projective synchronization problem, and the estimation of settling time has been verified less conservative than previous results. Finally, numerical examples are provided to demonstrate the effectiveness of the derived theoretical results.

Suggested Citation

  • Meng Hui & Chen Wei & Jiao Zhang & Herbert Ho-Ching Iu & Ni Luo & Rui Yao & Lin Bai, 2020. "Finite-Time Projective Synchronization of Fractional-Order Memristive Neural Networks with Mixed Time-Varying Delays," Complexity, Hindawi, vol. 2020, pages 1-27, June.
    • Handle: RePEc:hin:complx:4168705
    DOI: 10.1155/2020/4168705
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    Cited by:

    1. Yang, Shuai & Hu, Cheng & Yu, Juan & Jiang, Haijun, 2021. "Projective synchronization in finite-time for fully quaternion-valued memristive networks with fractional-order," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    2. Zhu, Sha & Bao, Haibo, 2022. "Event-triggered synchronization of coupled memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 415(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).

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