Abstract and Applied Analysis
Volume 2013 (2013), Article ID 540951, 7 pages
http://dx.doi.org/10.1155/2013/540951
Research Article

Novel Global Exponential Stability Criterion for Recurrent Neural Networks with Time-Varying Delay

Wenguang Luo,1,2 Xiuling Wang,1 Yonghua Liu,1 and Hongli Lan3

1School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
2Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou 545006, China
3School of Computer Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China

Received 15 October 2012; Accepted 3 January 2013

Academic Editor: Massimo Furi

Copyright © 2013 Wenguang Luo 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 problem of global exponential stability for recurrent neural networks with time-varying delay is investigated. By dividing the time delay interval [ ] into dynamical subintervals, a new Lyapunov-Krasovskii functional is introduced; then, a novel linear-matrix-inequality (LMI-) based delay-dependent exponential stability criterion is derived, which is less conservative than some previous literatures (Zhang et al., 2005; He et al., 2006; and Wu et al., 2008). An illustrate example is finally provided to show the effectiveness and the advantage of the proposed result.