Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering

Analog Circuit Fault Diagnosis Based on Deep Learning

Authors
Dezan Zhao, Jun Xing, Zhisen Wang
Corresponding Author
Dezan Zhao
Available Online October 2016.
DOI
https://doi.org/10.2991/mmme-16.2016.58How to use a DOI?
Keywords
Deep learning; analog circuits; fault diagnosis; neural network
Abstract

Deep learning is a new field in machine learning research, whose motivation is to build neural network simu-lating the human brain to analyze. Stacked autoencoder, which is a style of deep learning structure, is used to solve analog circuit fault diagnosis problem. An experiment is done, whose results show that the method pro-posed can effectively work on analog circuit fault diagnosis using neural network model based on the deep learning theory.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
Series
Advances in Engineering Research
Publication Date
October 2016
ISBN
978-94-6252-221-3
ISSN
2352-5401
DOI
https://doi.org/10.2991/mmme-16.2016.58How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Dezan Zhao
AU  - Jun Xing
AU  - Zhisen Wang
PY  - 2016/10
DA  - 2016/10
TI  - Analog Circuit Fault Diagnosis Based on Deep Learning
BT  - Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering
PB  - Atlantis Press
SP  - 254
EP  - 256
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmme-16.2016.58
DO  - https://doi.org/10.2991/mmme-16.2016.58
ID  - Zhao2016/10
ER  -