OPT OpenIR  > 光谱成像技术研究室
Extreme learning machine and genetic algorithm in quantitative analysis of sulfur hexafluoride by infrared spectroscopy
Liu, Huan1,2,3; Zhu, Jun4; Yin, Huan4; Yan, Qiangqiang1,2; Liu, Hong1,2; Guan, Shouxin1,2,3; Cai, Qisheng5; Sun, Jiawen6; Yao, Shun4; Wei, Ruyi1,2,3,7
作者部门光谱成像技术研究室
2022-04-01
发表期刊APPLIED OPTICS
ISSN1559-128X;2155-3165
卷号61期号:10页码:2834-2841
产权排序1
摘要

Owing to the general disadvantages of traditional neural networks in gas concentration inversion, such as slow training speed, sensitive learning rate selection, unstable solutions, weak generalization ability, and an ability to easily fall into local minimum points, the extreme learning machine (ELM) was applied to sulfur hexafluoride (SF6) concentration inversion research. To solve the problems of high dimensionality, collinearity, and noise of the spectral data input to the ELM network, a genetic algorithm was used to obtain fewer but critical spectral data. This was used as an input variable to achieve a genetic algorithm joint extreme learning machine (GA-ELM) whose performance was compared with the genetic algorithm joint backpropagation (GA-BP) neural network algorithm to verify its effectiveness. The experiment used 60 groups of SF6 gas samples with different concentrations, made via a self-developed Fourier transform infrared spectroscopy instrument. The SF6 gas samples were placed in an open optical path to obtain infrared interference signals, and then spectral restoration was performed. Fifty groups were randomly selected as training samples, and 10 groups were used as test samples. The BP neural network and ELM algorithms were used to invert the SF6 gas concentration of the mixed absorbance spectrum, and the results of the two algorithms were compared. The sample mean square error decreased from 248.6917 to 63.0359; the coefficient of determination increased from 0.9941 to 0.9984; and the single running time decreased from 0.0773 to 0.0042 s. Comparing the optimized GA-ELM algorithm with traditional algorithms such as ELM and partial least squares, the GA-ELM algorithm had higher prediction accuracy and operating efficiency and better stability and generalization performance in the quantitative analysis of small samples of gas under complex noise backgrounds. (C) 2022 Optica Publishing Group

DOI10.1364/AO.450805
收录类别SCI ; EI
语种英语
WOS记录号WOS:000778797800050
出版者Optica Publishing Group
EI入藏号20221511953938
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/95814
专题光谱成像技术研究室
通讯作者Wei, Ruyi
作者单位1.CAS Key Lab Spectral Imaging Technol, Xian 710119, Peoples R China
2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
3.Univ Chinese Acad Sci, Sch Optoelect, Beijing 100049, Peoples R China
4.DFH Satellite Co Ltd, Opt, Beijing 100094, Peoples R China
5.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
6.Qingdao Guoke Hongcheng Optoelect Technol Co Ltd, Qingdao 266114, Peoples R China
7.Wuhan Univ, Elect Informat Sch, Wuhan 430072, Peoples R China
推荐引用方式
GB/T 7714
Liu, Huan,Zhu, Jun,Yin, Huan,et al. Extreme learning machine and genetic algorithm in quantitative analysis of sulfur hexafluoride by infrared spectroscopy[J]. APPLIED OPTICS,2022,61(10):2834-2841.
APA Liu, Huan.,Zhu, Jun.,Yin, Huan.,Yan, Qiangqiang.,Liu, Hong.,...&Wei, Ruyi.(2022).Extreme learning machine and genetic algorithm in quantitative analysis of sulfur hexafluoride by infrared spectroscopy.APPLIED OPTICS,61(10),2834-2841.
MLA Liu, Huan,et al."Extreme learning machine and genetic algorithm in quantitative analysis of sulfur hexafluoride by infrared spectroscopy".APPLIED OPTICS 61.10(2022):2834-2841.
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
Extreme learning mac(5064KB)期刊论文出版稿限制开放CC BY-NC-SA请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Liu, Huan]的文章
[Zhu, Jun]的文章
[Yin, Huan]的文章
百度学术
百度学术中相似的文章
[Liu, Huan]的文章
[Zhu, Jun]的文章
[Yin, Huan]的文章
必应学术
必应学术中相似的文章
[Liu, Huan]的文章
[Zhu, Jun]的文章
[Yin, Huan]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。