Peng Pai, Zhao Xiaojin, Pan Xiaofang, Ye Wenbin
School of Electronic Science and Technology, Shenzhen University, Shenzhen 518060, China.
Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel). 2018 Jan 8;18(1):157. doi: 10.3390/s18010157.
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).
在这项工作中，我们提出了一种专门为气体分类量身定制的新型深度卷积神经网络（DCNN）。受DCNN在计算机视觉领域取得的巨大成功启发，我们设计了一个多达38层的DCNN。一般来说，所提出的气体神经网络，名为GasNet，由以下部分组成：六个卷积块，每个块由六层组成；一个池化层；以及一个全连接层。这些不同的层共同构成了一个用于气体分类的强大深度模型。实验结果表明，所提出的DCNN方法是一种对电子鼻数据进行分类的有效技术。我们还证明，DCNN方法能够提供比可比的支持向量机（SVM）方法和多层感知器（MLP）更高的分类准确率。
