Global Synchronization of Coupled Fractional-Order Recurrent Neural Networks

Peng Liu, Zhigang Zeng, Jun Wang

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)

Abstract

This paper presents new theoretical results on the global synchronization of coupled fractional-order recurrent neural networks. Under the assumptions that the coupled fractional-order recurrent neural networks are sequentially connected in form of a single spanning tree or multiple spanning trees, two sets of sufficient conditions are derived for ascertaining the global synchronization by using the properties of Mittag-Leffler function and stochastic matrices. Compared with existing works, the results herein are applicable for fractional-order systems, which could be viewed as an extension of integer-order ones. Two numerical examples are presented to illustrate the effectiveness and characteristics of the theoretical results.

Original languageEnglish
Article number8587128
Pages (from-to)2358-2368
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume30
Issue number8
DOIs
Publication statusPublished - Aug 2019
Externally publishedYes

Keywords

  • Fractional-order recurrent neural networks
  • sequential connectivity
  • synchronization

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