On-Line Modeling Via Fuzzy Support Vector Machines

被引：0
|
作者：
Tovar, Julio Cesar [1 ]
Yu, Wen [1 ]
机构：
[1] CINVESTAV, IPN, Dept Automat Control, Mexico City 07360, DF, Mexico
来源：
MICAI 2008: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2008年 / 5317卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper describes a novel nonlinear modeling approach by on-line clustering; fuzzy rules and support vector machine. Structure identification is realized by an on-line clustering method and fuzzy support vector machines; the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, the upper bounds of the modeling errors are proven.
引用
收藏
页码：220 / 229
页数：10
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