Mean-square asymptotic stability of stochastic inertial neural networks with time-delay and Markovian jump parameters
type: Item
instance of: scholarly article
title: Mean-square asymptotic stability of stochastic inertial neural networks with time-delay and Markovian jump parameters
zbMATH Open document ID: 1482.93674
DOI: 10.1504/IJDSDE.2021.120047
author: R. Krishnasamy
author: Raju K. George
published in: International Journal of Dynamical Systems and Differential Equations
publication date: 2022-03-14T00:00:00Z
review text: Summary: This paper investigates the stability of inertial neural networks (INNs) which incorporates the effects of both intrinsic and extrinsic noises along with time-delay. These intrinsic and extrinsic noises are taken to be in the form of Markovian jump parameters and Brownian motion respectively. Required sufficient stability conditions are established in the form of linear matrix inequalities from the construction of Lyapunov-Krasovskii functional. Derived conditions will be delay-dependent which includes information about the bounds of the time-delay and also its derivatives. Theory of Lyapunov stability, Ito calculus and linear matrix inequality are used to derive the main results. Numerical example is given to demonstrate the validity of the derived theoretical results.
Mathematics Subject Classification ID: 93E15
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 60G65
Mathematics Subject Classification ID: 60J65
Mathematics Subject Classification ID: 60J74
Mathematics Subject Classification ID: 37H30
zbMATH DE Number: 7488821
zbMATH Keywords: INNs
zbMATH Keywords: inertial neural networks
zbMATH Keywords: mean-square asymptotic stability
zbMATH Keywords: time-delay
zbMATH Keywords: Markovian jump
zbMATH Keywords: Lyapunov-Krasovskii functional
Wikidata QID: Q115237303
MaRDI profile type: Publication
full work available at URL: ijdsde.2021.120047
OpenAlex ID: W4206307694
description: scientific article; zbMATH DE number 7488821