Journal of Applied Mathematics
Volume 2012 (2012), Article ID 182745, 8 pages
http://dx.doi.org/10.1155/2012/182745
Research Article

LMI-Based Approach for Exponential Robust Stability of High-Order Hopfield Neural Networks with Time-Varying Delays

Yangfan Wang1 and Linshan Wang2,3

1College of Marine Life Science, Ocean University of China, Qingdao 266071, China
2Department of Mathematics, Ocean University of China, Qingdao 266071, China
3Department of Mathematics, Liaocheng University, Liaocheng 252059, China

Received 30 January 2012; Accepted 27 March 2012

Academic Editor: Wan-Tong Li

Copyright © 2012 Yangfan Wang and Linshan Wang. 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

This paper studies the problems of global exponential robust stability of high-order hopfield neural networks with time-varying delays. By employing a new Lyapunov-Krasovskii functional and linear matrix inequality, some criteria of global exponential robust stability for the high-order neural networks are established, which are easily verifiable and have a wider adaptive.