Two algebraic criteria for input-to-state stability of recurrent neural networks with time-varying delays

被引：0
|
作者：
Song Zhu
Yi Shen
机构：
[1] China University of Mining and Technology,College of Sciences
[2] Huazhong University of Science and Technology,Department of Control Science and Engineering
来源：
Neural Computing and Applications | 2013年 / 22卷
关键词：
Input-to-state stability; Recurrent neural network; Time-varying delay;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper presents two algebraic criteria for the input-to-state stability of recurrent neural networks with time-varying delays. The criteria which also ensure global exponential stability when the input u(t) is equal to 0 and is easy to be verified only with the connection weights of the recurrent neural networks. Two numerical examples are given to demonstrate the effectiveness of the proposed criteria.
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收藏
页码：1163 / 1169
页数：6
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