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Finite-time synchronisation of delayed fractional-order coupled neural networks

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  • Shuailei Zhang
  • Xinge Liu
  • Xuemei Li

Abstract

This paper considers the global synchronisation and finite-time synchronisation for a class of delayed fractional-order complex neural networks (DFOCNNs). Based on the properties of fractional-order calculus and the Razumikhin-type Lyapunov theorem of a fractional-order system, two new lemmas are proved. These lemmas are employed to formulate a couple of novel criteria for both finite-time synchronisation and global synchronisation of DFOCNNs. Moreover, the upper bound of the setting time for synchronisation is given. Three examples are provided to verify the effectiveness of the obtained results.

Suggested Citation

  • Shuailei Zhang & Xinge Liu & Xuemei Li, 2022. "Finite-time synchronisation of delayed fractional-order coupled neural networks," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(12), pages 2597-2611, September.
    • Handle: RePEc:taf:tsysxx:v:53:y:2022:i:12:p:2597-2611
    DOI: 10.1080/00207721.2022.2067910
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    Cited by:

    1. Saravanan Shanmugam & Rajarathinam Vadivel & Nallappan Gunasekaran, 2023. "Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation," Mathematics, MDPI, vol. 11(10), pages 1-24, May.

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