Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks

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 techniq...

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Main Authors: Chuangxia Huang, Jie Cao, Peng Wang
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2016/4958217
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author Chuangxia Huang
Jie Cao
Peng Wang
author_facet Chuangxia Huang
Jie Cao
Peng Wang
author_sort Chuangxia Huang
collection DOAJ
description 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.
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institution Kabale University
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spelling doaj-art-1e66d0ee98554d2c9d955b26917e2d872025-08-20T03:54:43ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2016-01-01201610.1155/2016/49582174958217Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural NetworksChuangxia Huang0Jie Cao1Peng Wang2School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, Hunan 410114, ChinaSchool of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, Hunan 410114, ChinaSchool of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, Hunan 410114, ChinaWe 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.http://dx.doi.org/10.1155/2016/4958217
spellingShingle Chuangxia Huang
Jie Cao
Peng Wang
Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks
Discrete Dynamics in Nature and Society
title Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks
title_full Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks
title_fullStr Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks
title_full_unstemmed Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks
title_short Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks
title_sort attractor and boundedness of switched stochastic cohen grossberg neural networks
url http://dx.doi.org/10.1155/2016/4958217
work_keys_str_mv AT chuangxiahuang attractorandboundednessofswitchedstochasticcohengrossbergneuralnetworks
AT jiecao attractorandboundednessofswitchedstochasticcohengrossbergneuralnetworks
AT pengwang attractorandboundednessofswitchedstochasticcohengrossbergneuralnetworks