Discrete Dynamics in Nature and Society
Volume 2009 (2009), Article ID 874582, 23 pages
doi:10.1155/2009/874582
New improved exponential stability criteria for discrete-time neural networks with time-varying delay
Zixin Liu1
, Shu Lv1
, Shouming Zhong1
and Mao Ye4
1School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054, China
4School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
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.