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

• Autorzy: Cui J. , Wang Y.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

analog circuit; fault classification; support vector machines classifier; fault dictionary; kernel parameter

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2011
• Tom: Vol. 18, nr 4
• Strony: 569--582
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., wzory

Twórcy

autor

Cui J.

autor

Wang Y.

• College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing City, Jiangsu Province, China,

Bibliografia

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