Comparison of SVM-fuzzy modelling techniques for system identification

被引：0

作者：

García-Gamboa, A ^{[1
]}

González-Mendoza, M ^{[1
]}

Ibarra-Orozco, R ^{[1
]}

Hernández-Gress, N ^{[1
]}

Mora-Vargas, J ^{[1
]}

机构：

[1] Intelligent Syst Res Grp, Mexico City 52926, DF, Mexico

来源：

MICAI 2005: ADVANCES IN ARTIFICIAL INTELLIGENCE | 2005年 / 3789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：

In recent years, the importance of the construction of fuzzy models from measured data has increased. Nevertheless, the complexity of real-life process is characterized by nonlinear and non-stationary dynamics, leaving so much classical identification techniques out of choice. In this paper, we present a comparison of Support Vector Machines (SVMs) for density estimation (SVDE) and for regression (SVR), versus traditional techniques as Fuzzy C-means and Gustafson-Kessel (for clustering) and Least Mean Squares (for regression), in order to find the parameters of Takagi-Sugeno (TS) fuzzy models. We show the properties of the identification procedure in a waste-water treatment database.

引用

页码：494 / 503

页数：10

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