Passivity properties of neuro identifier

被引：0
|
作者：
Yu, W [1 ]
Li, XO [1 ]
机构：
[1] IPN, CINVESTAV, Dept Control Automat, Mexico City 07360, DF, Mexico
来源：
PROCEEDINGS OF THE 39TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5 | 2000年
关键词：
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：
In this paper the passivity approach is applied to access several stability properties of neuro identifier. A dynamic neural network is used for nonlinear system no-line identification. By using a simple gradient learning law, the conditions for passivity, stability, asymptotic stable and input-to-state stability are established. We get a very interesting result: gradient algorithm is robust with respect to all kinds of bounded uncertainties for neuro identifier.
引用
收藏
页码：3848 / 3853
页数：6
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