Discrete Dynamics in Nature and Society
Volume 2009 (2009), Article ID 874582, 23 pages
doi:10.1155/2009/874582
Research Article

New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay

Zixin Liu,1,2 Shu Lv,1 Shouming Zhong,1 and Mao Ye3

1School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, China
2School of Mathematics and Statistics, Guizhou College of Finance and Economics, Guiyang 550004, China
3School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

Received 13 March 2009; Accepted 11 May 2009

Academic Editor: Manuel De La Sen

Copyright © 2009 Zixin Liu 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

The robust stability of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By decomposing some connection weight matrices, new Lyapunov-Krasovskii functionals are constructed, and serial new improved stability criteria are derived. These criteria are formulated in the forms of linear matrix inequalities (LMIs). Compared with some previous results, the new results are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method.