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

Peng Liu, Zhigang Zeng, Jun Wang

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8961184
Pages (from-to)4956-4967
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume31
Issue number11
DOIs
Publication statusPublished - Nov 2020
Externally publishedYes

Keywords

  • Filippov solution
  • finite-Time cluster synchronization
  • fractional-order neural networks

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