Discrete Dynamics in Nature and Society
Volume 2010 (2010), Article ID 368379, 19 pages
Research Article

Boundedness and Stability for Discrete-Time Delayed Neural Network with Complex-Valued Linear Threshold Neurons

1Department of Mathematics, Chongqing Jiaotong University, Chongqing 400074, China
2College of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Received 21 April 2010; Accepted 30 June 2010

Academic Editor: Josef Diblik

Copyright © 2010 Chengjun Duan and Qiankun Song. 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.


The discrete-time delayed neural network with complex-valued linear threshold neurons is considered. By constructing appropriate Lyapunov-Krasovskii functionals and employing linear matrix inequality technique and analysis method, several new delay-dependent criteria for checking the boundedness and global exponential stability are established. Illustrated examples are also given to show the effectiveness and less conservatism of the proposed criteria.