期刊
SENSORS
卷 18, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/s18010157
关键词
gas classification; deep convolutional neural networks; electronic nose
资金
- National Natural Science Foundation of China [61504087]
- Fundamental Research Foundation of Shenzhen [JCYJ20160308094919279, JCYJ20170302151123005]
- Key Project Department of Education of Guangdong Province [2015KQNCX142]
- Natural Science Foundation of Shenzhen University [2016020]
In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据