Analog Circuit Fault Classification Using Improved One-Against-One Support Vector Machines

Metrology and Measurement Systems

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