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Finite-Time Projective Synchronization of Caputo Type Fractional Complex-Valued Delayed Neural Networks

Author

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  • Shuang Wang

    (School of Mathematics and Physics, Anqing Normal University, Anqing 246133, China)

  • Hai Zhang

    (School of Mathematics and Physics, Anqing Normal University, Anqing 246133, China)

  • Weiwei Zhang

    (School of Mathematics and Physics, Anqing Normal University, Anqing 246133, China
    Department of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Hongmei Zhang

    (School of Mathematics and Physics, Anqing Normal University, Anqing 246133, China)

Abstract

This paper focuses on investigating the finite-time projective synchronization of Caputo type fractional-order complex-valued neural networks with time delay (FOCVNNTD). Based on the properties of fractional calculus and various inequality techniques, by constructing suitable the Lyapunov function and designing two new types controllers, i.e., feedback controller and adaptive controller, two sufficient criteria are derived to ensure the projective finite-time synchronization between drive and response systems, and the synchronization time can effectively be estimated. Finally, two numerical examples are presented to verify the effectiveness and feasibility of the proposed results.

Suggested Citation

  • Shuang Wang & Hai Zhang & Weiwei Zhang & Hongmei Zhang, 2021. "Finite-Time Projective Synchronization of Caputo Type Fractional Complex-Valued Delayed Neural Networks," Mathematics, MDPI, vol. 9(12), pages 1-14, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:12:p:1406-:d:576819
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    References listed on IDEAS

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    1. Qin, Xiaoli & Wang, Cong & Li, Lixiang & Peng, Haipeng & Yang, Yixian & Ye, Lu, 2018. "Finite-time modified projective synchronization of memristor-based neural network with multi-links and leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 302-315.
    2. Xu, Yao & Li, Wenxue, 2020. "Finite-time synchronization of fractional-order complex-valued coupled systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Hu, Taotao & He, Zheng & Zhang, Xiaojun & Zhong, Shouming, 2020. "Finite-time stability for fractional-order complex-valued neural networks with time delay," Applied Mathematics and Computation, Elsevier, vol. 365(C).
    4. Xu, Wei & Zhu, Song & Fang, Xiaoyu & Wang, Wei, 2019. "Adaptive anti-synchronization of memristor-based complex-valued neural networks with time delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    5. Zhang, Weiwei & Zhang, Hai & Cao, Jinde & Zhang, Hongmei & Chen, Dingyuan, 2020. "Synchronization of delayed fractional-order complex-valued neural networks with leakage delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    6. 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).
    7. Zhang, Yuting & Yu, Yongguang & Cui, Xueli, 2018. "Dynamical behaviors analysis of memristor-based fractional-order complex-valued neural networks with time delay," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 242-258.
    8. Qin, Xiaoli & Wang, Cong & Li, Lixiang & Peng, Haipeng & Yang, Yixian & Ye, Lu, 2019. "Finite-time projective synchronization of memristor-based neural networks with leakage and time-varying delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    9. Zhang, Hai & Ye, Miaolin & Ye, Renyu & Cao, Jinde, 2018. "Synchronization stability of Riemann–Liouville fractional delay-coupled complex neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 155-165.
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    Cited by:

    1. Zhang, Hai & Cheng, Jingshun & Zhang, Hongmei & Zhang, Weiwei & Cao, Jinde, 2021. "Quasi-uniform synchronization of Caputo type fractional neural networks with leakage and discrete delays★," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

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