Application of IWO-SVM Approach in Fault Diagnosis of Analog Circuits

被引：0

作者：

Cai, Shuxiang ^{[1
]}

Yuan, Haiwen ^{[1
]}

Lv, Jianxun ^{[1
]}

Cui, Yong ^{[1
]}

机构：

[1] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China

来源：

2013 25TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2013年

关键词：

Fault Diagnosis; SVM; IWO; IWO-SVM; Analog Circuits; SELECTION;

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：

Support vector machine (SVM) is a machine learning algorithm which has been applied to fault diagnosis of analog circuits. Invasive weed optimization (IWO) is a novel numerical optimization algorithm inspired from weed colonization. An approach that combines IWO and SVM (IWO-SVM) is proposed to fault diagnosis of analog circuits in this paper. The process of fault diagnosis of analog circuits using IWO-SVM approach is introduced in details. A biquadrate filter is used to test the performance of IWO-SVM approach for fault diagnosis. The simulation experiments show that the IWO-SVM approach proposed in this paper has a higher diagnosis accuracy rate than the conventional SVM in fault diagnosis of analog circuits.

引用

页码：4786 / 4791

页数：6

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