中国激光, 2018, 45 (9): 0911013, 网络出版: 2018-09-08   

基于特征提取的极限学习机算法在可调谐二极管激光吸收光谱学中的应用 下载: 760次

Application of Feature-Extraction-Based Extreme Learning Machine Algorithm in Tunable Diode Laser Absorption Spectroscopy
作者单位
中国石油大学(华东)信息与控制工程学院, 山东 青岛 266580
摘要
采用波长为1570 nm的激光器分析了天然气背景下的硫化氢气体, 通过自动化配气站产生了体积分数为0~10-4的硫化氢混合气体, 获取了92组稳定状态的光谱数据, 采用极限学习机(ELM)的回归模型反演了硫化氢浓度。把非线性迭代偏最小二乘法引入到光谱预处理中, 利用光谱特征参量与浓度参量建立了回归模型, 采用五折交叉校验的方法对模型进行了评估。测试结果显示, 光谱数据采用特征提取后的预测精度比直接用ELM进行回归的提升了25%, 且模型运算时间由0.12 s缩短到了10 ms以下。特征提取预处理缩短了ELM模型的训练时间, 提高了分析仪的分析精度和实时性。
Abstract
The laser with a wavelength of 1570 nm is used to analyze the hydrogen sulfide gas in the background of natural gas. The gas mixture containing the hydrogen sulfide with a volume fraction of 0-10-4 is produced in an automatic gas mixing station, and 92 groups of the spectral data with a stable state are obtained. The regression model of extreme learning machine (ELM) is adopted for the inversion calculation of the concentration of hydrogen sulfide. The nonlinear iteration partial least square (NIPALS) algorithm is introduced into the spectral pretreatment. The ELM regression model is established by using the spectral feature vector and the concentration vector, and is evaluated by the five-fold cross validation method. The test results show that, the regression predicating accuracy of the spectral data obtained by feature extraction is improved by 25% than that by direct ELM, and the model operation time is reduced from 0.12 s to less than 10 ms. The spectral pretreatment by the feature extraction can reduce the training time of ELM model and can also improve the analysis accuracy and the real-time capability of the analyzers.

吕晓翠, 李国林, 李晗, 季文海. 基于特征提取的极限学习机算法在可调谐二极管激光吸收光谱学中的应用[J]. 中国激光, 2018, 45(9): 0911013. Lü Xiaocui, Li Guolin, Li Han, Ji Wenhai. Application of Feature-Extraction-Based Extreme Learning Machine Algorithm in Tunable Diode Laser Absorption Spectroscopy[J]. Chinese Journal of Lasers, 2018, 45(9): 0911013.

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