Open Access
2013 Novel Global Exponential Stability Criterion for Recurrent Neural Networks with Time-Varying Delay
Wenguang Luo, Xiuling Wang, Yonghua Liu, Hongli Lan
Abstr. Appl. Anal. 2013: 1-7 (2013). DOI: 10.1155/2013/540951

Abstract

The problem of global exponential stability for recurrent neural networks with time-varying delay is investigated. By dividing the time delay interval [ 0 , τ ( t ) ] into K + 1 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.

Citation

Download Citation

Wenguang Luo. Xiuling Wang. Yonghua Liu. Hongli Lan. "Novel Global Exponential Stability Criterion for Recurrent Neural Networks with Time-Varying Delay." Abstr. Appl. Anal. 2013 1 - 7, 2013. https://doi.org/10.1155/2013/540951

Information

Published: 2013
First available in Project Euclid: 18 April 2013

zbMATH: 1274.34218
MathSciNet: MR3035201
Digital Object Identifier: 10.1155/2013/540951

Rights: Copyright © 2013 Hindawi

Vol.2013 • 2013
Back to Top