Analog Circuit Fault Diagnosis Based on Deep Learning

被引：0

作者：

Zhao, Dezan ^{[1
]}

Xing, Jun ^{[1
]}

Wang, Zhisen ^{[1
]}

机构：

[1] Dalian Polytech Univ, Inst Informat Sci & Engn, Dalian, Peoples R China

来源：

Proceedings of the 2016 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) | 2016年 / 79卷

关键词：

Deep learning; analog circuits; fault diagnosis; neural network; NETWORKS;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Deep learning is a new field in machine learning research, whose motivation is to build neural network simulating 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 proposed can effectively work on analog circuit fault diagnosis using neural network model based on the deep learning theory.

引用

页码：254 / 256

页数：3

共 11 条

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