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Fixed-time synchronization for delayed inertial complex-valued neural networks

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  • Long, Changqing
  • Zhang, Guodong
  • Hu, Junhao

Abstract

This paper researches the problem of p-norm fixed-time synchronization for a class of delayed inertial complex-valued neural networks (ICVNNs). By using reduced-order transformation and separating real and imaginary parts of complex-valued parameters, the second-order ICVNNs can be converted into the form of first-order real-valued differential equations. Then some new flexible and adjustable algebraic criteria to ensure the fixed-time synchronization of ICVNNs are established by means of the non-smooth Lyapunov function and inequality analytical techniques. Moreover, the settling time of fixed-time synchronization is theoretically estimated, which does not depend on the initial value of systems. Finally, simulation examples and applications are presented to illustrate the validity and availability of the obtained results.

Suggested Citation

  • Long, Changqing & Zhang, Guodong & Hu, Junhao, 2021. "Fixed-time synchronization for delayed inertial complex-valued neural networks," Applied Mathematics and Computation, Elsevier, vol. 405(C).
    • Handle: RePEc:eee:apmaco:v:405:y:2021:i:c:s0096300321003611
    DOI: 10.1016/j.amc.2021.126272
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    References listed on IDEAS

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    1. Zhang, Guodong & Zeng, Zhigang, 2018. "Exponential stability for a class of memristive neural networks with mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 544-554.
    2. Wang, Dongji & Chen, Fei & Meng, Bo & Hu, Xingliu & Wang, Jing, 2021. "Event-based secure H∞ load frequency control for delayed power systems subject to deception attacks," Applied Mathematics and Computation, Elsevier, vol. 394(C).
    3. Liu, Yu-An & Tang, Shengdao & Liu, Yufan & Kong, Qingkai & Wang, Jing, 2021. "Extended dissipative sliding mode control for nonlinear networked control systems via event-triggered mechanism with random uncertain measurement," Applied Mathematics and Computation, Elsevier, vol. 396(C).
    4. Du, Feifei & Lu, Jun-Guo, 2021. "New criterion for finite-time synchronization of fractional order memristor-based neural networks with time delay," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    5. Wu, Tianyu & Huang, Xia & Chen, Xiangyong & Wang, Jing, 2020. "Sampled-data H∞ exponential synchronization for delayed semi-Markov jump CDNs: A looped-functional approach," Applied Mathematics and Computation, Elsevier, vol. 377(C).
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    Citations

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    Cited by:

    1. Abdurahman, Abdujelil & Abudusaimaiti, Mairemunisa & Jiang, Haijun, 2023. "Fixed/predefined-time lag synchronization of complex-valued BAM neural networks with stochastic perturbations," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    2. 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).
    3. Pu, Hao & Li, Fengjun, 2023. "Fixed/predefined-time synchronization of complex-valued discontinuous delayed neural networks via non-chattering and saturation control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    4. Zheng, Yi & Wu, Xiaoqun & Fan, Ziye & Wang, Wei, 2022. "Identifying topology and system parameters of fractional-order complex dynamical networks," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    5. Guo, Runan & Xu, Shengyuan, 2023. "Observer-based sliding mode synchronization control of complex-valued neural networks with inertial term and mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 442(C).

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