Discrete Dynamics in Nature and Society
Volume 2009 (2009), Article ID 430158, 14 pages
Research Article

Delay-Range-Dependent Global Robust Passivity Analysis of Discrete-Time Uncertain Recurrent Neural Networks with Interval Time-Varying Delay

1Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, Taiwan
2Department of Biomechatronics Engineering, National Pingtung University of Science & Technology, Pingtung 912, Taiwan

Received 11 March 2009; Accepted 25 August 2009

Academic Editor: Guang Zhang

Copyright © 2009 Chien-Yu Lu 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.


This paper examines a passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with norm-bounded time-varying parameter uncertainties and interval time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. Based on an appropriate type of Lyapunov functional, sufficient passivity conditions for the DRNNs are derived in terms of a family of linear matrix inequalities (LMIs). Two numerical examples are given to illustrate the effectiveness and applicability.