Soft fault diagnosis of analog circuits based on semi-supervised support vector machine-Reference-Cited by-同舟云学术

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Soft fault diagnosis of analog circuits based on semi-supervised support vector machine

Published:2021-05-06 Issue:2 Volume:108 Page:305-315
ISSN:0925-1030
Container-title:Analog Integrated Circuits and Signal Processing
language:en
Short-container-title:Analog Integr Circ Sig Process

Author:

Wang L.,Tian H.,Zhang H.^ORCID

Funder

Henan province institution of higher learning youth backbone teachers training program

Natural Science Foundation of Henan Province

the Science and Technology Innovation Project of Henan Agricultural University

China Postdoctoral Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Surfaces, Coatings and Films,Hardware and Architecture,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s10470-021-01851-w.pdf

Reference34 articles.

1. Binu, D., & Kariyappa, B. S. (2017). A survey on fault diagnosis of analog circuits: taxonomy and state of the art. AEUE—International Journal of Electronics and Communications, 7(3), 68–83

2. Song, P., He, Y., & Cui, W. (2016). Statistical property feature extraction based on FRFT for fault diagnosis of analog circuits. Analog Integrated Circuits and Signal Processing, 87(3), 427–436

3. Min, M.A. (2016). Fault diagnosis technology of circuit and electronic system. Electronic Industry Press

4. Cui, Y., Shi, J., & Wang, Z. (2016). Analog circuit fault diagnosis based on quantum clustering based multi-valued quantum fuzzification decision tree (QC-MQFDT). Measurement, 9(3), 421–434

5. Cui, Y., Shi, J., & Wang, Z. (2016). Analog circuits fault diagnosis using multi-valued Fisher’s fuzzy decision tree (MFFDT). International Journal of Circuit Theory and Applications, 44(1), 240–260

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