Advances in Difference Equations
Volume 2007 (2007), Article ID 78160, 18 pages
Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms
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 -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.