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Passivity analysis of dynamic neural networks with different time-scales

被引：47
|
作者：
Yu, Wen
Li, Xiaoou
机构：
[1] CINVESTAV, IPN, Dept Automat Control, Mexico City 07360, DF, Mexico
[2] CINVESTAV, IPN, Dept Computac, Mexico City 07360, DF, Mexico
来源：
NEURAL PROCESSING LETTERS | 2007年 / 25卷 / 02期
关键词：
different time scales; neural networks; passivity; stability;
D O I：
10.1007/s11063-007-9034-0
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Dynamic neural networks with different time-scales include the aspects of fast and slow phenomenons. Some applications require that the equilibrium points of the designed networks are stable. In this paper, the passivity-based approach is used to derive stability conditions for dynamic neural networks with different time-scales. Several stability properties, such as passivity, asymptotic stability, input-to-state stability and bounded input bounded output stability, are guaranteed in certain senses. A numerical example is also given to demonstrate the effectiveness of the theoretical results.
引用
收藏
页码：143 / 155
页数：13
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