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

摘要：

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.

关键词： Delay-dependent stability discrete-time recurrent neural networks (DRNNs) linear matriz inequalities (LMIs) time-varying delays

作者: Xun-Lin Zhu Zhanlei Shang Hong-Yong Yang

作者单位: School of Computer Science and Communication Engineering, Zhengzhou University of Light Industry, Zh School of Computer Science and Technology, Ludong University, Yantai, 264025, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 4343-4348

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）