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Volume 29 Issue 9
Sep.  2003
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Journal of Beijing University of Aeronautics and Astronautics  > 2003  >  29(9): 789-792.
Wan Jiuqing, Li Xingshan. Analog circuits fault diagnosis based on serial support vector multi-classifier[J]. Journal of Beijing University of Aeronautics and Astronautics, 2003, 29(9): 789-792. (in Chinese)
Citation: Wan Jiuqing, Li Xingshan. Analog circuits fault diagnosis based on serial support vector multi-classifier[J]. Journal of Beijing University of Aeronautics and Astronautics, 2003, 29(9): 789-792. (in Chinese)
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Analog circuits fault diagnosis based on serial support vector multi-classifier

  • School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

  • Received Date: 10 Jul 2002
  • Publish Date: 30 Sep 2003
    Abstract
  • A new support vector multiclassification methodology was proposed where several binary support vector binary classifiers, each of which equipped with a feature extractor based on kernel principle components analysis (Kernel PCA), were organized in a serial structure. Its training and classification algorithm was described. The BP net classifier, RBF net classifier, traditional support vector multiclassifier and serial support vector multi-classifier (SSVC) were used for analog circuit fault diagnosis. Compared with BP net and RBF net classifiers, support vector approach led to significantly better classification accuracy on test patterns. The SSVC afforded top diagnosis accuracy among these classifiers and outperforms traditional support vector multicalssifier dramatically in training and classification efficiency.

     

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  • [1] Liu R W. Testing and diagnosis of analog circuits and systems[M]. NY:Van Nostrand Reinhold, 1991 [2] Spina R. Linear circuit fault diagnosis using neuromorphic analyzers . IEEE Trans on Circuits and Systems-Ⅱ:Analog and Digital Signal Processing, 1997, 44 (3):188~196 [3] Aminian M, Aminian F. Neural network based analog circuit fault diagnosis using wavelet transform as preprocessor[J]. IEEE Trans on Circuits and Systems-Ⅱ:Analog and Digital Signal Processing, 2000, 47(2):151~156 [4] Yang Z R. Applying a robust heteroscedastic probabilitistic neural network to analog fault detection and classification[J]. IEEE Trans on Circuits and Systems-Ⅱ:Analog and Digital Signal Processing, 2000, 19 (1):142~151 [5] Vapnik V N. The nature of statistical learning theory[M]. NY:Springer-Verlag, 1999 [6] Schoelkopf B, Smola A. Nonlinear components analysis as a kernel eigenvalue problem[J]. Neural Computation,1998,10(1):1299~1319
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