New delay-dependent stability criterion for discrete-time recurrent neural networks with time-varying delay

Authors:
Xun-Lin Zhu;Zhanlei Shang;Hong-Yong Yang
Affiliations:
School of Computer Science and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China;School of Computer Science and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China;School of Computer Science and Technology, Ludong University, Yantai, 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 paper studies the problem of stability analysis for discrete-time recurrent neural networks (DRNNs) with time-varying delays. Under a weak assumption on the activation functions, by defining a more general type of Lyapunov functionals and using a convex combination technique, a new delay-dependent stability criterion is proposed to guarantee the stability and uniqueness of equilibrium point of DRNNs in terms of linear matrix inequalities (LMIs). Compared with the existing results, the newly obtained stability condition is less conservative. A numerical example is given to illustrate the effectiveness and the benefits of the proposed method.