Metrology and Measurement Systems | |
Analog Circuit Fault Classification Using Improved One-Against-One Support Vector Machines | |
Jiang Cui1  Youren Wang1  | |
关键词: analog circuit; fault classification; Support Vector Machines classifier; fault dictionary; kernel parameter; | |
DOI : 10.2478/v10178-011-0055-7 | |
来源: Versita | |
【 摘 要 】
This paper presents a novel strategy of fault classification for the analog circuit under test (CUT). The proposed classification strategy is implemented with the one-against-one Support Vector Machines Classifier (SVC), which is improved by employing a fault dictionary to accelerate the testing procedure. In our investigations, the support vectors and other relevant parameters are obtained by training the standard binary support vector machines. In addition, a technique of radial-basis-function (RBF) kernel parameter evaluation and selection is invented. This technique can find a good and proper kernel parameter for the SVC prior to the machine learning. Two typical analog circuits are demonstrated to validate the effectiveness of the proposed method.
【 授权许可】
Unknown
【 预 览 】
Files | Size | Format | View |
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RO201912080706840ZK.pdf | 733KB | download |