Clustering for nonlinear system identification

被引：0

作者：

Rubio Avila, Jose De Jesus ^{[1
]}

Ramirez, Andres Ferreyra ^{[1
]}

Aviles-Cruz, Carlos ^{[1
]}

Vazquez-Alvarez, Ivan ^{[1
]}

机构：

[1] Univ Autonoma Metropolitana, Unidad Azcapotzalco, Dept Elect, Area Instrumentac, Av San Pablo 180 Col Reynosa Tamaulipas Azcapotza, Mexico City 02200, DF, Mexico

来源：

PROCEEDINGS OF THE 12TH WSEAS INTERNATIONAL CONFERENCE ON COMPUTERS , PTS 1-3: NEW ASPECTS OF COMPUTERS | 2008年

关键词：

Clustering algorithm; Fuzzy systems; Modeling; Identification;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We propose a new on-line clustering fuzzy neural network. Learning structure and parameter learning are updated at the same time in our algorithm, we do not make difference in structure learning and parameter learning. It generates groups with a given radius. The center is updated in order to get that the center is near to the incoming data in each iteration, in this way, It does not need to generate a new rule in each iteration, i.e., it does not generate many rules and It does not need to prune the rules.

引用

页码：616 / +

页数：2

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