Skip to main navigation Skip to search Skip to main content

Synchronization of fractional complex-valued neural networks with pantograph delays and inhibitory factors

  • Yao Xu
  • , Haodong Wang
  • , Jintong Yu
  • , Wenxue Li*
  • *Corresponding author for this work
  • Harbin Institute of Technology Weihai
  • Shanghai Jiao Tong University

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, the global synchronization of fractional complex-valued neural networks with pantograph delays and inhibitory factors is investigated, and some sufficient conditions are obtained. It deserves to mention that the attained sufficient conditions show that some factors including the pantograph coefficient, coupling connection weights, inhibitory factors, and the order of fractional derivative have an influence on network synchronization. Moreover, when complex-valued neural networks degenerate into real-valued cases and inhibitory factors are not taken into consideration, they are also discussed, and two corollaries are derived. Finally, to validate the feasibility of the theoretical results, a numerical example is presented. Meanwhile, some contrastive numerical results are given to illustrate the relationships among network synchronization, pantograph coefficient, and inhibitory factors.

Original languageEnglish
Article number126797
JournalNeurocomputing
Volume559
DOIs
StatePublished - 28 Nov 2023
Externally publishedYes

Keywords

  • Fractional complex-valued neural networks
  • Inhibitory factors
  • Pantograph delays
  • Synchronization

Fingerprint

Dive into the research topics of 'Synchronization of fractional complex-valued neural networks with pantograph delays and inhibitory factors'. Together they form a unique fingerprint.

Cite this