混合高斯过程回归模型在铁水硅含量预报中的应用 = Composite gaussian process regression model and its application to prediction of silicon content in hot metal
为了提高基于高斯过程回归的软测量模型的预测精度,提出了一种混合高斯过程回归模型。该模型将高斯过程回归模型预测输出值的方差及其分布作为主要考虑因素,对多个高斯过程回归模型的输出值进行组合输出,获得了比单个高斯过程回归模型更高的预测精度和更强的模型鲁棒性。将该模型实用于高炉铁水硅含量预报模型的建模,获得了比使用单个高斯过程回归模型建模时更好的应用效果。In order to increase the predictive precision of gaussian process regression based soft sensor, a composite gaussian process...
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| Other Authors: | |
| Format: | Journal Article |
| Language: | Chinese Chinese |
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2015
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| Online Access: | https://hdl.handle.net/10356/107138 http://hdl.handle.net/10220/25398 |
| _version_ | 1811695392881901568 |
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| author | 任江洪 Ren, Jiang-Hong 陈韬 Chen, Tao 曹长修 Cao, Chang-Xiu |
| author2 | School of Chemical and Biomedical Engineering |
| author_facet | School of Chemical and Biomedical Engineering 任江洪 Ren, Jiang-Hong 陈韬 Chen, Tao 曹长修 Cao, Chang-Xiu |
| author_sort | 任江洪 Ren, Jiang-Hong |
| collection | NTU |
| description | 为了提高基于高斯过程回归的软测量模型的预测精度,提出了一种混合高斯过程回归模型。该模型将高斯过程回归模型预测输出值的方差及其分布作为主要考虑因素,对多个高斯过程回归模型的输出值进行组合输出,获得了比单个高斯过程回归模型更高的预测精度和更强的模型鲁棒性。将该模型实用于高炉铁水硅含量预报模型的建模,获得了比使用单个高斯过程回归模型建模时更好的应用效果。In order to increase the predictive precision of gaussian process regression based soft sensor, a composite gaussian process regression model is proposed. This model combines the outputs of several gaussian process models as the output according to the variances and the distribution of the outputs, which results in higher prediction accuracy and higher robustness than the single gaussian process model. The proposed composite gaussian process regression model is successfully applied to the prediction of silicon content in hot metal. |
| first_indexed | 2024-10-01T07:22:45Z |
| format | Journal Article |
| id | ntu-10356/107138 |
| institution | Nanyang Technological University |
| language | Chinese Chinese |
| last_indexed | 2024-10-01T07:22:45Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/1071382023-12-29T06:52:07Z 混合高斯过程回归模型在铁水硅含量预报中的应用 = Composite gaussian process regression model and its application to prediction of silicon content in hot metal 任江洪 Ren, Jiang-Hong 陈韬 Chen, Tao 曹长修 Cao, Chang-Xiu School of Chemical and Biomedical Engineering DRNTU::Science::Medicine::Biomedical engineering 为了提高基于高斯过程回归的软测量模型的预测精度,提出了一种混合高斯过程回归模型。该模型将高斯过程回归模型预测输出值的方差及其分布作为主要考虑因素,对多个高斯过程回归模型的输出值进行组合输出,获得了比单个高斯过程回归模型更高的预测精度和更强的模型鲁棒性。将该模型实用于高炉铁水硅含量预报模型的建模,获得了比使用单个高斯过程回归模型建模时更好的应用效果。In order to increase the predictive precision of gaussian process regression based soft sensor, a composite gaussian process regression model is proposed. This model combines the outputs of several gaussian process models as the output according to the variances and the distribution of the outputs, which results in higher prediction accuracy and higher robustness than the single gaussian process model. The proposed composite gaussian process regression model is successfully applied to the prediction of silicon content in hot metal. Published version 2015-04-14T01:34:52Z 2019-12-06T22:25:30Z 2015-04-14T01:34:52Z 2019-12-06T22:25:30Z 2012 2012 Journal Article Ren, J.-H., Chen, T., & Cao C.-X. (2012). Composite gaussian process regression model and its application to prediction of silicon content in hot metal. Journal of Chongqing University, 35(2), 123-127. 1000-582X https://hdl.handle.net/10356/107138 http://hdl.handle.net/10220/25398 10.11835/j.issn.1000-582X.2012.02.020 Chinese zh Journal of Chongqing University © 2012 重庆大学学报期刊社. This paper was published in Journal of Chongqing University and is made available as an electronic reprint (preprint) with permission of 重庆大学学报期刊社. The paper can be found at the following official DOI: [http://dx.doi.org/10.11835/j.issn.1000-582X.2012.02.020]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law. application/pdf |
| spellingShingle | DRNTU::Science::Medicine::Biomedical engineering 任江洪 Ren, Jiang-Hong 陈韬 Chen, Tao 曹长修 Cao, Chang-Xiu 混合高斯过程回归模型在铁水硅含量预报中的应用 = Composite gaussian process regression model and its application to prediction of silicon content in hot metal |
| title | 混合高斯过程回归模型在铁水硅含量预报中的应用 = Composite gaussian process regression model and its application to prediction of silicon content in hot metal |
| title_full | 混合高斯过程回归模型在铁水硅含量预报中的应用 = Composite gaussian process regression model and its application to prediction of silicon content in hot metal |
| title_fullStr | 混合高斯过程回归模型在铁水硅含量预报中的应用 = Composite gaussian process regression model and its application to prediction of silicon content in hot metal |
| title_full_unstemmed | 混合高斯过程回归模型在铁水硅含量预报中的应用 = Composite gaussian process regression model and its application to prediction of silicon content in hot metal |
| title_short | 混合高斯过程回归模型在铁水硅含量预报中的应用 = Composite gaussian process regression model and its application to prediction of silicon content in hot metal |
| title_sort | 混合高斯过程回归模型在铁水硅含量预报中的应用 composite gaussian process regression model and its application to prediction of silicon content in hot metal |
| topic | DRNTU::Science::Medicine::Biomedical engineering |
| url | https://hdl.handle.net/10356/107138 http://hdl.handle.net/10220/25398 |
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