TY - JOUR N2 - In order to make the analog fault classification more accurate, we present a method based on the Support Vector Machines Classifier (SVC) with wavelet packet decomposition (WPD) as a preprocessor. In this paper, the conventional one-against-rest SVC is resorted to perform a multi-class classification task because this classifier is simple in terms of training and testing. However, this SVC needs all decision functions to classify the query sample. In our study, this classifier is improved to make the fault classification task more fast and efficient. Also, in order to reduce the size of the feature samples, the wavelet packet analysis is employed. In our investigations, the wavelet analysis can be used as a tool of feature extractor or noise filter and this preprocessor can improve the fault classification resolution of the analog circuits. Moreover, our investigation illustrates that the SVC can be applicable to the domain of analog fault classification and this novel classifier can be viewed as an alternative for the back-propagation (BP) neural network classifier. L1 - http://www.journals.pan.pl/Content/107073/PDF/Journal10178-VolumeXVII%20Issue4_05%20paper.pdf L2 - http://www.journals.pan.pl/Content/107073 PY - 2010 IS - No 4 EP - 581 DO - 10.2478/v10178-010-0046-0 KW - analog circuits KW - fault classification KW - Support Vector Machines Classifier KW - Neural Networks KW - wavelet packet decomposition A1 - Cui, Jiang A1 - Wang, Youren PB - Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation DA - 2010 T1 - A Novel Approach of Analog Fault Classification Using a Support Vector Machines Classifier SP - 561 UR - http://www.journals.pan.pl/dlibra/publication/edition/107073 T2 - Metrology and Measurement Systems ER -