Application of Least Squares Support Vector Machine in Fault Diagnosis

被引：0

作者：

Zhang, Yongli ^{[1
]}

Zhu, Yanwei ^{[2
]}

Lin, Shufei ^{[3
]}

Liu, Xiaohong ^{[1
]}

机构：

[1] Hebei United Univ, Coll Light Ind, Tangshan, Peoples R China

[2] Tangshan Normal Univ, Dept Math & Informat sci, Tangshan, Hebei, Peoples R China

[3] North Univ Ethn, Dept Comp, Yinchuan, Peoples R China

来源：

INFORMATION COMPUTING AND APPLICATIONS, PT II | 2011年 / 244卷

关键词：

Support Vector Machine; Least squares; Proximal Support Vector Machine; Fault Diagnosis;

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 daily life fault diagnosis is widely used production. With the rapid development of science and technology, the new high-tech products emerged. It is not enough data of samples. Conventional approach is ineffective. It is need to find a good method. The least squares support vector machine algorithm and the proximal of support vector machine applied to fault diagnosis. Through experiments when learning samples is not enough, equipment failure does not reduce and the classification accuracy has increased even. On fault diagnosis the training speed has been to improve and the cost of building has been reduced. Improve overall system performance of fault diagnosis.

引用

页码：192 / +

页数：3

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