Stability analysis for recurrent neural networks with time-varying delay

This paper is concerned with the stability analysis for static recurrent neural networks (RNNs) with time-varying delay. By Lyapunov functional method and linear matrix inequality technique, some new delay-dependent conditions are established to ensure the asymptotic stability of the neural network. Expressed in linear matrix inequalities (LMIs), the proposed delay-dependent stability conditions can be checked using the recently developed algorithms. A numerical example is given to show that the obtained conditions can provide less conservative results than some existing ones. This work was supported by National Natural Science Foundation of China (No. 60674027). Yuan-Yuan Wu received the B. Sc. and M. Sc. degrees from the College of Mathematics and Information at Henan Normal University, Xinxiang, PRC in 2003 and 2006, respectively. She is currently a Ph.D. candidate in School of Automation, Southeast University, Nanjing, PRC. Her research interests include singular systems, delayed systems, and neural networks. Yu-Qiang Wu received the Ph.D. degree in automatic control from Southeast University, Nanjing, PRC in 1994. Currently, he is a professor in the Institute of Automation, Qufu normal University, Qufu, PRC. His research interests include variable structure control and nonlinear system control.