Journal of Inequalities and Applications
Volume 2009 (2009), Article ID 385298, 20 pages
Research Article

Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay

1School of Mathematics and Statistics, Zhejiang University of Finance & Economical, Hangzhou 310012, China
2School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China
3School of Mathematical Science and Computing Technology, Central South University, Changsha 410083, China

Received 22 March 2009; Revised 7 July 2009; Accepted 2 October 2009

Academic Editor: Alexander I. Domoshnitsky

Copyright © 2009 Yi Wang 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.


We derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory (BAM) neural networks with periodic coefficients and distributed delay, and find that the criterion relies on the Lipschitz constants of the signal transmission functions, weights of the neural network, and delay kernels. The proposed model transforms the original interacting network into matrix analysis problem which is easy to check, thereby significantly reducing the computational complexity and making analysis of periodic oscillation for even large-scale networks.