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ファイル | 記述 | サイズ | フォーマット | |
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j.compchemeng.2014.12.016.pdf | 289.38 kB | Adobe PDF | 見る/開く |
タイトル: | Input variable scaling for statistical modeling |
著者: | Kim, Sanghong Kano, Manabu https://orcid.org/0000-0002-2325-1043 (unconfirmed) Nakagawa, Hiroshi Hasebe, Shinji https://orcid.org/0000-0003-0956-5051 (unconfirmed) |
著者名の別形: | 金, 尚弘 |
キーワード: | Statistical model Soft sensor Input variable scaling Pharmaceutical process Distillation process |
発行日: | Mar-2015 |
出版者: | Elsevier Ltd. |
誌名: | Computers & Chemical Engineering |
巻: | 74 |
開始ページ: | 59 |
終了ページ: | 65 |
抄録: | Input variable scaling is one of the most important steps in statistical modeling. However, it has not been actively investigated, and autoscaling is mostly used. This paper proposes two input variable scaling methods for improving the accuracy of soft sensors. One method statistically derives the input variable scaling factors; the other one uses spectroscopic data of a material whose content is estimated by the soft sensor. The proposed methods can determine the scales of the input variables based on their importance in output estimation. Thus, it can reduce the negative effects of input variables which are not related to an output variable. The effectiveness of the proposed methods was confirmed through a numerical example and industrial applications to a pharmaceutical and a distillation processes. In the industrial applications, the proposed methods improved the estimation accuracy by up to 63% compared to conventional methods such as autoscaling with input variable selection. |
著作権等: | © 2015 Elsevier Ltd. This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 |
URI: | http://hdl.handle.net/2433/193671 |
DOI(出版社版): | 10.1016/j.compchemeng.2014.12.016 |
出現コレクション: | 学術雑誌掲載論文等 |
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