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Discrete Dynamics in Nature and Society
Volume 2016, Article ID 4958217, 19 pages
Research Article

Attractor and Boundedness of Switched Stochastic Cohen-Grossberg Neural Networks

1School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, Hunan 410114, China
2Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

Received 15 February 2016; Accepted 5 April 2016

Academic Editor: Guoqiang Hu

Copyright © 2016 Chuangxia Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.