引用本文:宋江婷,金福江,周丽春.透射光谱线性空间核学习建模求解多组分浓度[J].控制理论与应用,2024,41(3):468~473.[点击复制]
SONG Jiang-ting,JIN Fu-jiang,ZHOU Li-chun.Kernel learning modeling for solving multi-component concentrations with linear space of transmission spectra[J].Control Theory and Technology,2024,41(3):468~473.[点击复制]
透射光谱线性空间核学习建模求解多组分浓度
Kernel learning modeling for solving multi-component concentrations with linear space of transmission spectra
摘要点击 2303  全文点击 68  投稿时间：2022-09-30  修订日期：2023-05-15
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DOI编号  10.7641/CAT.2023.20863
  2024,41(3):468-473
中文关键词  多组分  浓度  量子遂穿  线性系统
英文关键词  multi-component system  concentration  quantum Tunneling  linear systems
基金项目  福建省科技计划项目(2021H6028), 流程工业综合自动化国家重点实验室联合开放基金项目(2022–KF–21–04)资助.
作者单位邮编
宋江婷 华侨大学机电及自动化学院 361021
金福江* 华侨大学机电及自动化学院 361021
周丽春 华侨大学机电及自动化学院 
中文摘要
      本文对求解多组分体系浓度的分光光度计同时测定法提供了一种建模方法. 通过对多组分体系的透射光 谱建立光谱线性空间, 证明了多组分体系的分子势函数矩阵VM可以转化为对角矩阵, 对角矩阵的对角元是单组分 势函数的块矩阵Ji. 多组分体系透射光谱空间是单组分分子透射光谱函数为基函数、分子数量占比为坐标的线性 组合. 利用多核学习法确定各单组分体系透射波的占比权重系数, 提出了用单组分浓度光量子隧穿软测量模型测定 多组分浓度的测量方法. 实例验证表明此方法稳定可靠, 且能降低计算的复杂性.
英文摘要
      A modeling method is proposed in this paper for simultaneous spectrophotometric determination of multicomponent system concentrations. A linear space of transmission spectra of the multi-component system was established, which proved that the potential function matrix VM of the multi-component system can be transformed into a diagonal matrix, in which the diagonal elements are blocked matrix Ji of the single-component potential functions. The multicomponent system transmission spectral space is a linear combination of the transmission spectral functions of each singlecomponent molecule as the basis function and the proportion of molecular numbers as the coordinate. The weight coefficients of the transmission waves of each single-component system were determined using the multi-kernel learning method, and a measurement method for measuring multi-component concentration using a single-component concentration quantum tunneling soft measurement model was proposed. Example validation shows that this method is stable and reliable and can reduce computational complexity.