Global stability of Hopfield neural networks under dynamical thresholds with distributed delays
type: Item
instance of: scholarly article
title: Global stability of Hopfield neural networks under dynamical thresholds with distributed delays
zbMATH Open document ID: 1106.92008
author: Fei-Yu Zhang
author: Haifeng Huo
published in: Discrete Dynamics in Nature and Society
publication date: 2007-03-19T00:00:00Z
review text: Summary: We study the dynamical behavior of a class of Hopfield neural networks with distributed delays under dynamical thresholds. Some new criteria ensuring the existence, uniqueness, and global asymptotic stability of equilibrium point sare derived. In the results, we do not require the activation functions to satisfy a Lipschitz condition, and also not to be bounded, differentiable, or monotone nondecreasing. Moreover, the symmetry of the connection matrix is also not necessary. Thus, our results improve some previous works in the literature. These conditions have great importance in designs and applications of the global asymptotic stability for Hopfield neural networks involving distributed delays under dynamical thresholds.
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 37N25
Mathematics Subject Classification ID: 34K20
Mathematics Subject Classification ID: 34D23
Mathematics Subject Classification ID: 45J05
Mathematics Subject Classification ID: 34K25
zbMATH DE Number: 5134616
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 5134616