Research Article | OPEN ACCESS
A Novel Fault Feature Extraction Method of Analog Circuit Based on Improved KPCA
1He Xing, 1Wang Hong-li, 1Lu Jing-hui and 2Sun Guo-qiang
1Xi’an Research Institute of Hi-Tech., Hongqing Town, Xi’an 710025, China
2Aviation University of Air Force, Changchun, 130022, China
Research Journal of Applied Sciences, Engineering and Technology 2013 22:5314-5319
Received: November 08, 2012 | Accepted: January 05, 2013 | Published: May 25, 2013
Abstract
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.
Keywords:
Between-class scatter matrix, feature extraction, fisher criterion, KPCA, within-class scatter matrix,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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