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Discrete Dynamics in Nature and Society
Volume 2016 (2016), Article ID 2762960, 11 pages
Research Article

Exponential Stability of Cohen-Grossberg Neural Networks with Impulse Time Window

College of Mathematics and System Sciences, Xinjiang University, Urumqi, Xinjiang 830046, China

Received 29 March 2016; Accepted 19 May 2016

Academic Editor: Zhengqiu Zhang

Copyright © 2016 Mei Liu 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.


This paper concerns the problem of exponential stability for a class of Cohen-Grossberg neural networks with impulse time window and time-varying delays. In our letter, the impulsive effects we considered can stochastically occur at a definitive time window and the impulsive controllers we considered can be nonlinear and even rely on the states of all the neurons. Hence, the impulses here can be more applicable and more general. By utilizing Lyapunov functional theory, inequality technique, and the analysis method, we obtain some novel and effective exponential stability criteria for the Cohen-Grossberg neural networks. These results generalize a few previous known results and numerical simulations are given to show the effectiveness of the derived results.