Analog Circuit Fault Diagnosis Based on Deep Learning
- https://doi.org/10.2991/mmme-16.2016.58How to use a DOI?
- Deep learning; analog circuits; fault diagnosis; neural network
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.
- © 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 -