Global exponential stability analysis for recurrent neural networks with time-varying delay

Authors:
Xiaoli Guo;Qingbo Li;Yonggang Chen;Yuanyuan Wu
Affiliations:
Department of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou, China;Department of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou, China;Department of Mathematics, Henan Institute of Science Technology, Xinxiang, China;School of Automation, Southeast University, Nanjing, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

This letter deals with the exponential stability problem for static recurrent neural networks (RNNs) with time-varying delay. By Lyapunov functional method and linear matrix inequality technique, some novel delay-dependent criteria are established to ensure the exponential stability of the considered neural network. The proposed exponential stability criteria are expressed in terms of linear matrix inequalities, and can be checked using the recently developed algorithms. A numerical example is given to show that the obtained criteria can provide less conservative results than some existing ones.