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Global Mittag-Leffler synchronization of discrete-time fractional-order neural networks with time delays

Author

Listed:
  • Zhang, Xiao-Li
  • Li, Hong-Li
  • Kao, Yonggui
  • Zhang, Long
  • Jiang, Haijun

Abstract

In this article, the problem of the global Mittag-Leffler synchronization is proposed for a sort of discrete-time fractional-order neural networks (DFNNs) with delays. In the first place, a flesh power law inequality pertaining to fractional difference is constructed by means of integration by parts, Young inequality, and some properties about fractional-order difference. In addition, based on aforesaid inequalities, Lyapunov function theory and properties of nabla Mittag-Leffler function as well as inequality techniques, some plentiful criteria are formed to achieve the global Mittag-Leffler synchronization for the delayed DFNNs via devising novel adaptive controller and delay feedback controller. In the end, numerical modeling is given to demonstrate effectiveness of theoretical verdicts.

Suggested Citation

  • Zhang, Xiao-Li & Li, Hong-Li & Kao, Yonggui & Zhang, Long & Jiang, Haijun, 2022. "Global Mittag-Leffler synchronization of discrete-time fractional-order neural networks with time delays," Applied Mathematics and Computation, Elsevier, vol. 433(C).
    • Handle: RePEc:eee:apmaco:v:433:y:2022:i:c:s009630032200491x
    DOI: 10.1016/j.amc.2022.127417
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    References listed on IDEAS

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    7. Wang, Mei & Jia, Baoguo & Chen, Churong & Zhu, Xiaojuan & Du, Feifei, 2020. "Discrete fractional Bihari inequality and uniqueness theorem of solutions of nabla fractional difference equations with non-Lipschitz nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 376(C).
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    Cited by:

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    3. Kiruthika, R. & Krishnasamy, R. & Lakshmanan, S. & Prakash, M. & Manivannan, A., 2023. "Non-fragile sampled-data control for synchronization of chaotic fractional-order delayed neural networks via LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
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