Advances in Difference Equations
Volume 2007 (2007), Article ID 78160, 18 pages
Research Article

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Qiankun Song1 and Jinde Cao2

1Department of Mathematics, Chongqing Jiaotong University, \newline Chongqing 400074, China
2Department of Mathematics, Southeast University, Nanjing 210096, China

Received 9 March 2007; Accepted 16 May 2007

Academic Editor: Ulrich Krause

Copyright © 2007 Qiankun Song and Jinde Cao. 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.


Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.