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Optimization of IBBCEAS Spectral Retrieval Range Based on Machine Learning and Genetic Algorithm

文献类型：期刊论文

作者Ling Liu-yi2,3; Huang You-rui1,2; Wang Chen-jun2; Hu Ren-zhi3; Li Ang3; Xie Pin-hua3
刊名SPECTROSCOPY AND SPECTRAL ANALYSIS
出版日期2021-06-01
卷号41
关键词Incoherent broadband cavity enhanced absorption spectroscopy Optimization Retrieval range Machine learning Genetic algorithm
ISSN号1000-0593
DOI10.3964/j.issn.1000-0593(2021)06-1869-05
通讯作者Ling Liu-yi(lyling@aust.edu.cn) ; Huang You-rui(hyr628@163.com)
英文摘要Incoherent broadband cavity-enhanced absorption spectroscopy (IBBCEAS) can highly sensitively detect trace gases by using an optical resonator to enhance the absorption path. At present, IBBCEAS mainly uses light-emitting diode (LED) as its incoherent light source. The fitting result of the measured gas concentration with improper spectral retrieval range may have a large deviation if the reflectivity curve of the resonator' s mirror does not match well with the LED radiation spectrum with limited bandwidth. Taking the case of quantitative detection for atmospheric NO2, the influence of retrieval range on NO2 fitting results is analyzed. It is found that the relative fitting error will increase rapidly when the retrieval range is narrowed to a certain extent. In this paper, a method for optimizing retrieval range based on machine learning using RBF neural network and genetic algorithm is proposed in order to minimize the error. 435 sample data are obtained by retrieving NO2 concentrations with various spectral subranges , which are members of 430 similar to 480 nm and have different widths and center wavelengths. 80% of the sample data are used to train the RBF neural network, and the rest for the testing network. The nonlinear mapping relationship between input parameters, starting and ending wavelengths of retrieval range, and output parameter, relative fitting error, is obtained by the trained network. The optimal retrieval range is searched using a genetic algorithm, in which starting and ending wavelength of the retrieval range are encoded into an individual, and a population is generated with a number of random individuals. After the evolution of multi-generation populations, the optimal retrieval range is obtained by the genetic algorithm, which uses the output of the RBF neural network, i. e. relative fitting error, as individual fitness. Every population has 100 individuals, and the maximum evolution generation is set to 100. When the populations evolve in the 61st generation, the optimal individual, corresponding to 445. 78 similar to 479.44 nm of the optimal retrieval range, appears, and the optimal fitness is 3. 584%. The NO2 fitting results with the other four typical and the optimal retrieval ranges with the same width are compared. The results show that fitting error, relative fitting error and standard deviation of fitting residual with the optimal retrieval range are lower than those with the other four retrieval ranges. The results demonstrate the feasibility of using machine learning to determine the optimal retrieval range of an IBBCEAS system.
WOS研究方向Spectroscopy
语种英语
WOS记录号WOS:000671195700034
出版者OFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/124321]  
专题中国科学院合肥物质科学研究院
通讯作者Ling Liu-yi; Huang You-rui
作者单位1.Anhui Sci & Technol Univ, Fengyang 233100, Peoples R China
2.Anhui Univ Sci & Technol, Sch Artificial Intelligence, Huainan 232001, Peoples R China
3.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Environm Opt & Technol, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Ling Liu-yi,Huang You-rui,Wang Chen-jun,et al. Optimization of IBBCEAS Spectral Retrieval Range Based on Machine Learning and Genetic Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS,2021,41.
APA Ling Liu-yi,Huang You-rui,Wang Chen-jun,Hu Ren-zhi,Li Ang,&Xie Pin-hua.(2021).Optimization of IBBCEAS Spectral Retrieval Range Based on Machine Learning and Genetic Algorithm.SPECTROSCOPY AND SPECTRAL ANALYSIS,41.
MLA Ling Liu-yi,et al."Optimization of IBBCEAS Spectral Retrieval Range Based on Machine Learning and Genetic Algorithm".SPECTROSCOPY AND SPECTRAL ANALYSIS 41(2021).

入库方式： OAI收割

来源：合肥物质科学研究院

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