Analog Circuit Fault Diagnosis Based on Deep Learning

DOI

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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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  - 10.2991/mmme-16.2016.58
ID  - Zhao2016/10
ER  -