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

被引：0
|
作者：
Xu, Yong [1 ]
Zhu, Song [2 ]
机构：
[1] Cent South Univ, Sch Math Sci & Comp Technol, Changsha 410075, Hunan, Peoples R China
[2] China Univ Mining & Technol, Coll Sci, Xuzhou 221116, Peoples R China
来源：
PROCEEDINGS OF THE 2012 24TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2012年
关键词：
Input-to-State stability; Recurrent Neural Network; Time-Varying Delay; Markov Chain; GLOBAL EXPONENTIAL STABILITY; ROBUST STABILITY; DISCRETE;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
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 verified with the connection weights. A numerical example is given to demonstrate the effectiveness of the proposed criteria.
引用
收藏
页码：1897 / 1900
页数：4
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