Journal of Inequalities and Applications
Volume 2009 (2009), Article ID 491268, 17 pages
doi:10.1155/2009/491268
Research Article

Global Exponential Stability of Delayed Cohen-Grossberg BAM Neural Networks with Impulses on Time Scales

Yongkun Li,1 Yuchun Hua,1 and Yu Fei2

1Department of Mathematics, Yunnan University, Kunming, Yunnan 650091, China
2School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, China

Received 18 April 2009; Accepted 14 July 2009

Academic Editor: Patricia J. Y. Wong

Copyright © 2009 Yongkun Li 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.

Abstract

Based on the theory of calculus on time scales, the homeomorphism theory, Lyapunov functional method, and some analysis techniques, sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of the equilibrium point of Cohen-Grossberg bidirectional associative memory (BAM) neural networks with distributed delays and impulses on time scales. This is the first time applying the time-scale calculus theory to unify the discrete-time and continuous-time Cohen-Grossberg BAM neural network with impulses under the same framework.