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2013 (Vol. 5, Issue: 22)
Article Information:

A Novel Fault Feature Extraction Method of Analog Circuit Based on Improved KPCA

He Xing, Wang Hong-li, Lu Jing-hui and Sun Guo-qiang
Corresponding Author:  He Xing 

Key words:  Between-class scatter matrix, feature extraction, fisher criterion, KPCA, within-class scatter matrix, ,
Vol. 5 , (22): 5314-5319
Submitted Accepted Published
November 08, 2012 January 05, 2013 May 25, 2013

The Kernel Principal Component Analysis (KPCA) extracts the principal components by computing the population variance, which doesn’t consider the difference between one class and the others. So, it makes against the fault diagnosis. For solving this problem, the study introduced Fisher classification function into The KPCA and proposed an improved FKPCA with the class information. Then, the algorithm was applied in analog-circuit fault feature extraction and the neural network was applied to diagnose the faults. The results indicate the classification effect of the principal components extracted by the algorithm is more better. It improves the rate of fault diagnosis and reduces the test time.
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  Cite this Reference:
He Xing, Wang Hong-li, Lu Jing-hui and Sun Guo-qiang, 2013. A Novel Fault Feature Extraction Method of Analog Circuit Based on Improved KPCA.  Research Journal of Applied Sciences, Engineering and Technology, 5(22): 5314-5319.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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