双模型结合进一步降低预测均方根误差和均方根相对误差的方法
前期研究工作提出了以预测均方根相对误差最小为回归目标的方法（Minimization of prediction relative error,MPRE）,它能使得预测结果的均方根相对误差更小。偏最小二乘法（Partial least squares,PLS）是以预测均方根误差为回归目标,能使得预测结果的均方根误差更小。基于多模型结合的思想,提出将MPRE与PLS相结合的双模型结合多元校正方法。本方法步骤为：（1）分别采用MPRE与PLS法对校正集建模;（2）计算阈值;（3）分别采用已建立好的MPRE与PLS模型进行预测;（4）将预测结果与阈值进行比较,得到预测结果。通过对酒精的近红外光谱与汽...
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| Published in | 分析化学 Vol. 43; no. 5; pp. 754 - 758 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
西北大学分析科学研究所,西安710069
2015
西安文理学院化学与化学工程学院,西安710065%第二炮兵工程大学,西安,710025%西北大学分析科学研究所,西安,710069 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0253-3820 |
| DOI | 10.11895/j.issn.0253-3820.140915 |
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| Abstract | 前期研究工作提出了以预测均方根相对误差最小为回归目标的方法（Minimization of prediction relative error,MPRE）,它能使得预测结果的均方根相对误差更小。偏最小二乘法（Partial least squares,PLS）是以预测均方根误差为回归目标,能使得预测结果的均方根误差更小。基于多模型结合的思想,提出将MPRE与PLS相结合的双模型结合多元校正方法。本方法步骤为：（1）分别采用MPRE与PLS法对校正集建模;（2）计算阈值;（3）分别采用已建立好的MPRE与PLS模型进行预测;（4）将预测结果与阈值进行比较,得到预测结果。通过对酒精的近红外光谱与汽油紫外光谱进行定量分析结果表明,本方法可进一步减小预测均方根误差与相对误差。 |
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| AbstractList | 前期研究工作提出了以预测均方根相对误差最小为回归目标的方法（Minimization of prediction relative error,MPRE）,它能使得预测结果的均方根相对误差更小。偏最小二乘法（Partial least squares,PLS）是以预测均方根误差为回归目标,能使得预测结果的均方根误差更小。基于多模型结合的思想,提出将MPRE与PLS相结合的双模型结合多元校正方法。本方法步骤为：（1）分别采用MPRE与PLS法对校正集建模;（2）计算阈值;（3）分别采用已建立好的MPRE与PLS模型进行预测;（4）将预测结果与阈值进行比较,得到预测结果。通过对酒精的近红外光谱与汽油紫外光谱进行定量分析结果表明,本方法可进一步减小预测均方根误差与相对误差。 前期研究工作提出了以预测均方根相对误差最小为回归目标的方法(Minimization of prediction relative error,MPRE),它能使得预测结果的均方根相对误差更小.偏最小二乘法(Partial least squares,PLS)是以预测均方根误差为回归目标,能使得预测结果的均方根误差更小.基于多模型结合的思想,提出将MPRE与PLS相结合的双模型结合多元校正方法.本方法步骤为:(1)分别采用MPRE与PLS法对校正集建模;(2)计算阈值;(3)分别采用已建立好的MPRE与PLS模型进行预测;(4)将预测结果与阈值进行比较,得到预测结果.通过对酒精的近红外光谱与汽油紫外光谱进行定量分析结果表明,本方法可进一步减小预测均方根误差与相对误差. |
| Author | 吴雪梅 刘志强 张天龙 李华 |
| AuthorAffiliation | 西北大学分析科学研究所,西安710069 西安文理学院化学与化学工程学院,西安710065 第二炮兵工程大学,西安710025 |
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| Author_FL | LIU Zhi-Qiang LI Hua WU Xue-Mei ZHANG Tian-Long |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DOI | 10.11895/j.issn.0253-3820.140915 |
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| DocumentTitleAlternate | A Method Based on Double Models Combination to Further Reduce Root-Mean-Square Error and Relative Error of Prediction |
| DocumentTitle_FL | A Method Based on Double Models Combination to Further Reduce Root-Mean-Square Error and Relative Error of Prediction |
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| GrantInformation_xml | – fundername: 国家自然科学基金; 高等学校博士学科点专项科研基金(No.20126101110019)资助 This work was supported by the National Natural Science Foundation of China; the Research Fund for the Doctoral Program of Higher Education,China funderid: (.21175106,21375105); (.21175106,21375105); (20126101110019) |
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| Keywords | 双模型 均方根相对误差 The root-mean-square relative error 多元校正 Double models Multivariate calibration 均方根误差 Root-mean-square error |
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| Notes | Double models; Multivariate calibration; The root-mean-square relative error; Root-mean-squareerror 22-1125/O6 WU Xue-Mei, LIU Zhi-Qiang, ZHANG Tian-Long , LI Hua 1 （ Institute of Analytical Science, Northwest University, Xi＇an 710069, China） 2（Department of Chemistry and Chemical Engineering, Xi＇an University, Xi＇an 710065, China） 3（ The Second Artillery Engineering University, Xi＇an 710025, China） A method for multivariate calibration with minimization of root-mean-square relative error of prediction （RMSREP） has been proposed in previous work, and the method in this paper is named MPRE method. MPRE is based on the use of back-propagation artificial neural network （BP-ANN）. The regression objective of MPRE method is to minimize RMSREP by changing the output values of BP-ANN. Partial least squares （PLS） model was widely used in analytical field as it can minimize the root-mean-square error of prediction （RMSEP）. A method based on double models combination that employed the idea of ensemble MPRE and PLS models i |
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| PublicationTitle | 分析化学 |
| PublicationTitleAlternate | Chinese Journal of Analytical Chemistry |
| PublicationTitle_FL | Chinese Journal of Analytical Chemistry |
| PublicationYear | 2015 |
| Publisher | 西北大学分析科学研究所,西安710069 西安文理学院化学与化学工程学院,西安710065%第二炮兵工程大学,西安,710025%西北大学分析科学研究所,西安,710069 |
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| Snippet | 前期研究工作提出了以预测均方根相对误差最小为回归目标的方法（Minimization of prediction relative error,MPRE）,它能使得预测结果的均方根相对误差更小。偏最小二乘... 前期研究工作提出了以预测均方根相对误差最小为回归目标的方法(Minimization of prediction relative error,MPRE),它能使得预测结果的均方根相对误差更小.偏最小二乘... |
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| Title | 双模型结合进一步降低预测均方根误差和均方根相对误差的方法 |
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