Attractor and boundedness of switched stochastic Cohen-Grossberg neural networks
type: Item
instance of: scholarly article
title: Attractor and boundedness of switched stochastic Cohen-Grossberg neural networks
zbMATH Open document ID: 1417.35009
DOI: 10.1155/2016/4958217
author: Jie Cao
author: Peng Wang
author: Chuangxia Huang
published in: Discrete Dynamics in Nature and Society
publication date: 2019-02-19T00:00:00Z
review text: Summary: We address the problem of stochastic attractor and boundedness of a class of switched Cohen-Grossberg neural networks (CGNN) with discrete and infinitely distributed delays. With the help of stochastic analysis technology, the Lyapunov-Krasovskii functional method, linear matrix inequalities technique (LMI), and the average dwell time approach (ADT), some novel sufficient conditions regarding the issues of mean-square uniformly ultimate boundedness, the existence of a stochastic attractor, and the mean-square exponential stability for the switched Cohen-Grossberg neural networks are established. Finally, illustrative examples and their simulations are provided to illustrate the effectiveness of the proposed results.
Mathematics Subject Classification ID: 35B41
Mathematics Subject Classification ID: 35R60
Mathematics Subject Classification ID: 60H10
zbMATH DE Number: 7025686
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/4958217
OpenAlex ID: W2407192197
Wikidata QID: Q59123531
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description: scientific article; zbMATH DE number 7025686