Global Ezponential Stability Analysis for Recurrent Neural Networks with Time-varying Delay

摘要：

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.

关键词： Static neural networks Time-varying delay Global ezponential stability Linear matriz inequalities (LMIs)

作者: Xiaoli Guo Qingbo Li Yonggang Chen Yuanyuan Wu

作者单位: Department of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou Department of Mathematics, Henan Institute of Science Technology, Xinxiang 453003, China School of Automation, Southeast University, Nanjing 210096, 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: 2976-2980

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）