Fault Isolation for Nonlinear Systems Using Flexible Support Vector Regression

被引：2
|
作者：
Liu, Yufang [1 ,2 ]
Jiang, Bin [1 ]
Yi, Hui [3 ]
Bo, Cuimei [3 ]
机构：
[1] Nanjing Univ Aeronaut & Astronaut, Coll Automat Engn, Nanjing 210016, Jiangsu, Peoples R China
[2] Guodian Environm Protect Res Inst, Dept Technol Res, Nanjing 210032, Jiangsu, Peoples R China
[3] Nanjing Univ Technol, Coll Automat & Elect, Nanjing 211816, Jiangsu, Peoples R China
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2014年 / 2014卷
基金：
美国国家科学基金会; 国家教育部博士点专项基金资助;
关键词：
NEURAL-NETWORKS; DIAGNOSIS; SELECTION; MACHINES;
D O I：
10.1155/2014/713018
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
While support vector regression is widely used as both a function approximating tool and a residual generator for nonlinear system fault isolation, a drawback for this method is the freedom in selecting model parameters. Moreover, for samples with discordant distributing complexities, the selection of reasonable parameters is even impossible. To alleviate this problem we introduce the method of flexible support vector regression (F-SVR), which is especially suited for modelling complicated sample distributions, as it is free from parameters selection. Reasonable parameters for F-SVR are automatically generated given a sample distribution. Lastly, we apply this method in the analysis of the fault isolation of high frequency power supplies, where satisfactory results have been obtained.
引用
收藏
页数：10
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