基于卷积神经网络的电子鼻分类识别  

Electronic Nose Classification Recognition based on Convolutional Neural Network

作　　者：吴青云 邹亚囡[2] 史雪莹 WU Qingyun;ZOU Yanan;SHI Xueying(School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin City 132022,China;School of Science,Jilin Institute of Chemical Technology,Jilin City 132022,China)

机构地区：[1]吉林化工学院信息与控制工程学院,吉林吉林132022 [2]吉林化工学院理学院,吉林吉林132022

出　　处：《吉林化工学院学报》2022年第11期38-41,共4页Journal of Jilin Institute of Chemical Technology

摘　　要：在混合气体识别的研究中,针对目前电子鼻应用于化工污染物种类监测时难以达到理想精度的问题,提出了一个基于卷积神经网络的气体分类识别算法.首先利用卷积神经网络的自适应特征提取能力,有效降低原始数据对后续操作的影响;其次进行多次实验训练,对卷积神经网络进行参数优化,提高网络模型性能;最后将提出的卷积神经网络算法与BP神经网络算法分别应用于加州大学公开数据集中一氧化碳和乙烯混合气体的实验数据中.实验结果表明,卷积神经网络算法对此数据集的气体种类检测准确率达到93%,比BP神经网络算法应用于气体识别时精度更高、误差更小,为电子鼻系统气体种类检测提供了一种新的方法.In the study of mixed gas identification,a gas classification recognition algorithm based on convolutional neural network was proposed to solve the problem that it is difficult to achieve ideal accuracy when electronic nose was used in the monitoring of chemical pollutant types.Firstly,the adaptive feature extraction ability of convolutional neural network was used to effectively reduce the impact of original data on subsequent operations.Secondly,several experiments were conducted to optimize the parameters of the convolutional neural network to improve the performance of the network model.Finally,the proposed convolutional neural network algorithm and BP neural network algorithm were applied to the experimental data of carbon monoxide and ethylene mixture gas in the public dataset of the University of California,respectively.The experimental results showed that the gas species detection accuracy of the convolutional neural network algorithm in this dataset reaches 93%,which was higher accuracy and smaller error than the BP neural network algorithm when applied to gas identification,which provided a new method for gas species detection in the electronic nose system.

关 键 词：电子鼻 卷积神经网络 气体识别 BP神经网络 气体传感器阵列 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]