A new decision tree approach of support vector machine for analog circuit fault diagnosis

被引：24
|
作者：
Ma, Qingfeng [1 ,2 ]
He, Yuzhu [1 ]
Zhou, Fuqiang [1 ]
机构：
[1] Beihang Univ, Beijing 100191, Peoples R China
[2] Huaibei Normal Univ, Huaibei 235000, Peoples R China
来源：
ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING | 2016年 / 88卷 / 03期
关键词：
Analog circuit fault diagnosis; Support vector machine; Decision tree approach; NEURAL-NETWORKS; MAHALANOBIS DISTANCE; ELECTRONIC-CIRCUITS; SVM; CLASSIFICATION; PREPROCESSOR; PARAMETERS;
D O I：
10.1007/s10470-016-0775-4
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes a new decision tree approach for analog circuit fault diagnosis using binary support vector machines (BSVMs) that are trained by using different data sets. To evaluate the performance of those different BSVMs, a new criterion is defined by considering the weighted number of fault-pairs that can be separated by one BSVM. Specifically, the weight of each fault-pair is inversely proportional to the number of BSVMs that can separate it. Simulation results by using two example circuits demonstrate that the proposed approach achieves better performance in terms of accuracy and efficiency than existing approaches.
引用
收藏
页码：455 / 463
页数：9
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