4.7 Article

Asymptotic and Finite-Time Cluster Synchronization of Coupled Fractional-Order Neural Networks With Time Delay

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2962006

Keywords

Synchronization; Neural networks; Complex networks; Delays; Calculus; Delay effects; Mathematical model; Filippov solution; finite-time cluster synchronization; fractional-order neural networks

Funding

  1. Research Grants Council, Hong Kong [11208517, 11202318]
  2. National Natural Science Foundation of China [61673330, 61703313]
  3. Foundation for Innovative Research Groups of Hubei Province of China [2017CFA005]
  4. 111 Project on Computational Intelligence and Intelligent Control [B18024]

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This article is devoted to the cluster synchronization issue of coupled fractional-order neural networks. By introducing the stability theory of fractional-order differential systems and the framework of Filippov regularization, some sufficient conditions are derived for ascertaining the asymptotic and finite-time cluster synchronization of coupled fractional-order neural networks, respectively. In addition, the upper bound of the settling time for finite-time cluster synchronization is estimated. Compared with the existing works, the results herein are applicable for fractional-order systems, which could be regarded as an extension of integer-order ones. A numerical example with different cases is presented to illustrate the validity of theoretical results.

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