Fixed-time stability of Cohen-Grossberg BAM neural networks with impulsive perturbations

Md Arzoo Jamal, Rakesh Kumar, Santwana Mukhopadhyay, Oh Min Kwon

Research output: Contribution to journalArticlepeer-review

Abstract

This article concerns the problem of fixed-time stability (FXTS) of a Cohen-Grossberg bidirectional associative memory neural network (CGBAMNN) with destabilizing impulsive effects. A novel sufficient condition for the impulsive dynamical systems (IDSs) to be FXTS for destabilizing impulses is obtained. Different from the usual Lyapunov inequality for FXTS of IDSs, we have applied a new Lyapunov inequality to obtain the results under impulsive perturbations. Based on the average impulsive interval (AII) and the comparison principle, we have derived the results of this paper. Two types of continuous controllers: one with signum terms and another without signum terms, based on a new Lyapunov inequality, FXTS of CGBAMNN have been studied. The settling-time functions obtained in this article depend on the parameters of the impulsive sequences. Finally, two numerical examples, one is a cyber-physical system with deception attacks and another is a neural network, are given to validate the efficiency of our obtained theoretical results.

Original languageEnglish
Article number126501
JournalNeurocomputing
Volume550
DOIs
StatePublished - 14 Sep 2023

Keywords

  • Average impulsive interval (AII)
  • BAM neural networks
  • Cohen-Grossberg neural networks
  • Fixed-time stability
  • Impulsive dynamical systems (IDSs)

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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