Exponential convergence of delayed cellular neural networks with time-varying coefficients
type: Item
instance of: scholarly article
title: Exponential convergence of delayed cellular neural networks with time-varying coefficients
zbMATH Open document ID: 1161.34354
author: Yi Tang
published in: Applied Mathematics Letters
publication date: 2008-11-06T00:00:00Z
review text: The paper investigates the convergence behavior of a cellular neural network with time-varying delays and continuously distributed delays. In the mathematical model of this class of cellular neural network, it is assumed that the neuron activation functions don't satisfy a Lipschitz condition, the time-varying delays are bounded, and the coefficients are time-varying. By applying some mathematical analytic techniques, they derive some new sufficient conditions ensuring that all solutions of the cellular neural network system converge exponentially to the equilibrium point. An example is carried out to illustrate the main results.
reviewed by: Qintao Gan
Mathematics Subject Classification ID: 34K25
Mathematics Subject Classification ID: 34K20
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 5363178
zbMATH Keywords: cellular neural networks
zbMATH Keywords: exponential convergence
zbMATH Keywords: time-varying coefficient
MaRDI profile type: Publication
full work available at URL: j.aml.2007.09.008
OpenAlex ID: W2021839635
cites work: Novel delay-dependent robust stability criterion of delayed cellular neural networks
cites work: Global exponential stability of cellular neural networks with variable delays
cites work: LMI-based criteria for global robust stability of bidirectional associative memory networks with time delay
cites work: Absolute exponential stability of recurrent neural networks with Lipschitz-continuous activation functions and time delays
DOI: 10.1016/J.AML.2007.09.008
description: scientific article; zbMATH DE number 5363178