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

页码：1897 / 1900

页数：4

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