Global finite-time stability of delayed quaternion-valued neural networks based on a class of extended Lyapunov–Razumikhin methods

Chengsheng Li, Jinde Cao, Ardak Kashkynbayev

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this paper, a class of global finite-time stability problem for quaternion-valued neural networks with time-varying delays are investigated by adopting an extended modification Lyapunov–Razumikhin (L–R) method and a new upper bounds estimation of system solution in terms of convergence rate was obtained. Firstly, a new extended method of L–R is proposed to solve the general difficulty to find a proper Lyapunov functional. Then, a new suitable controller is designed, the new conditions of inequalities global finite-time stability are obtained via combining with the former proposed L–R method in the separated real-valued system. Finally, for purpose of verifying the availability of the theorem presented, two given illustrative examples are shown.

Original languageEnglish
Pages (from-to) 729 - 739
Number of pages11
JournalCognitive Neurodynamics
Volume17
Issue number3
DOIs
Publication statusPublished - Jun 2023

Keywords

  • Global finite-time
  • Modified L–R method
  • Quaternion
  • Stability
  • Time varying delays

ASJC Scopus subject areas

  • Cognitive Neuroscience

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