Abstract and Applied Analysis
Volume 2012 (2012), Article ID 372324, 12 pages
http://dx.doi.org/10.1155/2012/372324
Research Article

Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays

Weisong Zhou and Zhichun Yang

Department of Mathematics, Chongqing Normal University, Chongqing 400047, China

Received 22 August 2012; Revised 22 December 2012; Accepted 22 December 2012

Academic Editor: Sabri Arik

Copyright © 2012 Weisong Zhou and Zhichun Yang. 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

A class of dynamical neural network models with time-varying delays is considered. By employing the Lyapunov-Krasovskii functional method and linear matrix inequalities (LMIs) technique, some new sufficient conditions ensuring the input-to-state stability (ISS) property of the nonlinear network systems are obtained. Finally, numerical examples are provided to illustrate the efficiency of the derived results.