Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 829037, 14 pages
Research Article

Further Stability Criterion on Delayed Recurrent Neural Networks Based on Reciprocal Convex Technique

1Key Laboratory of Measurement and Control of CSE, School of Automation, Southeast University, Ministry of Education, Nanjing 210096, China
2School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210007, China

Received 15 April 2011; Revised 3 June 2011; Accepted 6 July 2011

Academic Editor: Zidong Wang

Copyright © 2012 Guobao Zhang 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.


Together with Lyapunov-Krasovskii functional theory and reciprocal convex technique, a new sufficient condition is derived to guarantee the global stability for recurrent neural networks with both time-varying and continuously distributed delays, in which one improved delay-partitioning technique is employed. The LMI-based criterion heavily depends on both the upper and lower bounds on state delay and its derivative, which is different from the existent ones and has more application areas as the lower bound of delay derivative is available. Finally, some numerical examples can illustrate the reduced conservatism of the derived results by thinning the delay interval.