摘要
针对模拟电路故障诊断中特征提取的难题,引入深度信念网模型,在Leapfrog滤波器电路上对深度信念网、单隐层BP网络和三隐层BP网络三种模型做分类对比实验,实验证明了深度信念网应用的可行性和有效性.基于带通电路,设计实验对比了深度信念网、小波分析方法及单隐层BP网络的特征提取能力,结果表明深度信念网特征提取能力明显优于小波分析和单隐层BP网络,提取的特征更能反映数据本质.
In view of the problem of feature extraction for analog circuit fault diagnosis, model of deep belief network （DBN） is introduced in this paper. Comparison experiments on Leapfrog Filter with a group of models including DBN, single hidden layer BP and three hidden layer BP prove that the model of DBN is feasible and effective. Moreover, according to the experiment on band pass filters with methods of wavelet analysis, DBN and single hidden layer BP network, the results show that the ability of feature extraction of DBN is better and the feature of extraction is closer to the essence of data.
出处
《微电子学与计算机》
CSCD
北大核心
2016年第9期159-163,共5页
Microelectronics & Computer
关键词
深度学习
深度信念网
模拟电路
故障诊断
特征提取
deep learning
deep belief network
analog circuit
fault diagnosis
feature extraction
