Input-to-State Stability of Recurrent Neural Networks with Time-Varying Delays and Markovian Switching

摘要：

This paper presents an algebraic criterion for the input-to-state stability (ISS) of recurrent neural networks with Markovian switching. The criterion is easy to be veri.ed with the connection weights. A numerical example is given to demonstrate the effectiveness of the proposed criteria.

关键词： Input-to-State stability Recurrent Neural Network Time-Varying Delay Markov Chain

作者: Yong Xu Song Zhu

作者单位: School of Mathematical Sciences and ComputingTechnology, Central South University, Changsha 410075, College of Sciences, China University of Mining and Technology, Xuzhou 221116, China

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 1909-1912

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）