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Abstract and Applied Analysis
Volume 2013, Article ID 270791, 9 pages
Research Article

Asymptotic Behavior of Switched Stochastic Delayed Cellular Neural Networks via Average Dwell Time Method

1College of Mathematics and Computing Science, Changsha University of Science and Technology, Changsha, Hunan 410114, China
2Hunan Provincial Center for Disease Control and Prevention, Changsha, Hunan 410005, China
3School of Business, Hunan Normal University, Changsha, Hunan 410081, China

Received 28 January 2013; Accepted 31 March 2013

Academic Editor: Zhichun Yang

Copyright © 2013 Hanfeng Kuang 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.


The asymptotic behavior of a class of switched stochastic cellular neural networks (CNNs) with mixed delays (discrete time-varying delays and distributed time-varying delays) is investigated in this paper. Employing the average dwell time approach (ADT), stochastic analysis technology, and linear matrix inequalities technique (LMI), some novel sufficient conditions on the issue of asymptotic behavior (the mean-square ultimate boundedness, the existence of an attractor, and the mean-square exponential stability) are established. A numerical example is provided to illustrate the effectiveness of the proposed results.