Global convergence for Cohen-Grossberg neural networks with discontinuous activation functions
type: Item
instance of: scholarly article
title: Global convergence for Cohen-Grossberg neural networks with discontinuous activation functions
zbMATH Open document ID: 1261.34058
DOI: 10.1155/2012/109319
published in: Abstract and Applied Analysis
publication date: 2013-02-04T00:00:00Z
review text: Summary: Cohen-Grossberg neural networks with discontinuous activation functions are considered. Using \(M\)-matrices and a generalized Lyapunov-like approach, the uniqueness is proved for state solutions and corresponding output solutions and equilibrium points and corresponding output equilibrium points of the considered neural networks. Moreover, global exponential stability of equilibrium points is obtained. Furthermore, by the contraction mapping principle, uniqueness and global exponential stability of the limit cycles are proved. Finally, an example is given to illustrate the effectiveness of the obtained results.
Mathematics Subject Classification ID: 34K20
Mathematics Subject Classification ID: 34K25
Mathematics Subject Classification ID: 34K21
Mathematics Subject Classification ID: 47N20
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 34A36
zbMATH DE Number: 6134015
Wikidata QID: Q58694838
author: Yanyan Wang
author: Jian-Ping Zhou
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/109319
OpenAlex ID: W2051402752
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description: scientific article; zbMATH DE number 6134015