Network fault diagnosis based on Dual-SVM

被引：0

作者：

Wen, Xiang-Xi ^{[1
]}

Meng, Xiang-Ru ^{[1
]}

Ma, Zhi-Qiang ^{[1
]}

机构：

[1] Information and Navigation Institute, Air Force Engineering University, Xi'an 710077, China

来源：

Kongzhi yu Juece/Control and Decision | 2013年 / 28卷 / 04期

关键词：

Failure analysis - Fault detection;

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：

The dual support vector machine(Dual-SVM) is proposed to promote the speed of establishing model and diagnosing. The diagnosis model is established by two SVM training processes. In the first process, the approximate classifying hyperplane is directly obtained by the two classes centers and the distribution of the samples on the connecting direction of centers. In the second fuzzy SVM process, the boundary samples are selected, fuzzy memberships are calculated, and the diagnosis model is established. The experiments on DARPA data-sets show that the Dual-SVM can get higher training speed and more simplified model compared to SVM.

引用

页码：506 / 510

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