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Mittag-Leffler synchronization of fractional-order coupled neural networks with mixed delays

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  • Zheng, Bibo
  • Wang, Zhanshan

Abstract

This paper is devoted to investigating Mittag-Leffler synchronization of fractional-order coupled neural networks (FOCNNs) with mixed delays, where the time-varying delays and infinite distributed delays are both taken into account. Firstly, a universal delayed FOCNNs model is introduced, which includes several FOCNNs with various time delays structures such as distributed delays and constant delays or time-varying delays, as its special cases. Besides, for the case where the existing results cannot be applied to infinite time delays, a generalized fractional-order Halanay inequality is proposed, which facilitates the analysis of synchronization of FOCNNs. Based on the generalized inequality, a unified synchronization analysis framework for FOCNNs is established, which is also applied to the cases with commonly considered FOCNNs with distribute delays, constant or time-varying delays. All the derived synchronization criteria have similar results and can be easily verified. Finally, a numerical example is provided to show the effectiveness of obtained results.

Suggested Citation

  • Zheng, Bibo & Wang, Zhanshan, 2022. "Mittag-Leffler synchronization of fractional-order coupled neural networks with mixed delays," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    • Handle: RePEc:eee:apmaco:v:430:y:2022:i:c:s0096300322003770
    DOI: 10.1016/j.amc.2022.127303
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    1. Wang, Fei & Yang, Yongqing, 2018. "Quasi-synchronization for fractional-order delayed dynamical networks with heterogeneous nodes," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 1-14.
    2. Li, Hong-Li & Kao, Yonggui & Hu, Cheng & Jiang, Haijun & Jiang, Yao-Lin, 2021. "Robust exponential stability of fractional-order coupled quaternion-valued neural networks with parametric uncertainties and impulsive effects," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    3. Shah, Kamal & Alqudah, Manar A. & Jarad, Fahd & Abdeljawad, Thabet, 2020. "Semi-analytical study of Pine Wilt Disease model with convex rate under Caputo–Febrizio fractional order derivative," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Li, Tao & Tang, Xiaoling & Qian, Wei & Fei, Shumin, 2019. "Hybrid-delay-dependent approach to synchronization in distributed delay neutral neural networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 449-463.
    5. Yang, Xujun & Li, Chuandong & Huang, Tingwen & Song, Qiankun & Huang, Junjian, 2018. "Synchronization of fractional-order memristor-based complex-valued neural networks with uncertain parameters and time delays," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 105-123.
    6. Zhang, Weiwei & Sha, Chunlin & Cao, Jinde & Wang, Guanglan & Wang, Yuan, 2021. "Adaptive quaternion projective synchronization of fractional order delayed neural networks in quaternion field," Applied Mathematics and Computation, Elsevier, vol. 400(C).
    7. Yu, Xiangnan & Zhang, Yong & Sun, HongGuang & Zheng, Chunmiao, 2018. "Time fractional derivative model with Mittag-Leffler function kernel for describing anomalous diffusion: Analytical solution in bounded-domain and model comparison," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 306-312.
    8. Li, Hong-Li & Zhang, Long & Hu, Cheng & Jiang, Haijun & Cao, Jinde, 2020. "Global Mittag-Leffler synchronization of fractional-order delayed quaternion-valued neural networks: Direct quaternion approach," Applied Mathematics and Computation, Elsevier, vol. 373(C).
    9. Li, Liangliang & Jian, Jigui, 2015. "Exponential p-convergence analysis for stochastic BAM neural networks with time-varying and infinite distributed delays," Applied Mathematics and Computation, Elsevier, vol. 266(C), pages 860-873.
    10. Dang, Rongqi & Chen, Yiming, 2021. "Fractional modelling and numerical simulations of variable-section viscoelastic arches," Applied Mathematics and Computation, Elsevier, vol. 409(C).
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    Cited by:

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    2. Wang, Shasha & Jian, Jigui, 2023. "Predefined-time synchronization of incommensurate fractional-order competitive neural networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    3. Xing, Xiaofei & Wu, Huaiqin & Cao, Jinde, 2024. "Finite-time synchronization of impulsive stochastic systems with DoS attacks via dynamic event-triggered control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 219(C), pages 573-593.
    4. Cui, Xueke & Li, Hong-Li & Zhang, Long & Hu, Cheng & Bao, Haibo, 2023. "Complete synchronization for discrete-time fractional-order coupled neural networks with time delays," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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