書誌事項
- タイトル別名
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- Development of Nonlinear Soft Sensor Methods Considering Process Dynamics
- プロセス ノ ドウトクセイ オ コウリョ シタ ヒセンケイ ソフトセンサー シュホウ ノ カイハツ
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説明
Soft sensors have been widely used for process control in industrial plants to estimate difficult-to-measure process variables online. A genetic algorithm-based process variables and dynamics selection (GAVDS) method is one method used to select important process variables and optimal time-delays of each variable simultaneously. However, the GAVDS method cannot handle a nonlinear relationship between X and an objective variable y because linear regression is used as a modeling technique. We therefore proposed a region selection method based on GAVDSand support vector regression (SVR), which is a nonlinear regression method. The proposed method is named GAVDS-SVR. We applied GAVDS-SVR to simulation data having high correlation between close pairs of X-variables and a nonlinear relationship between X and y. The GAVDS-SVR method could select regions of X-variables appropriately by considering the nonlinearity and could construct predictive models with high accuracy. Through soft-sensor analysis of industrial polymer process data, we confirmed that predictive, easy-to-interpret, and appropriate models were constructed using the proposed method.
収録刊行物
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- 計測自動制御学会論文集
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計測自動制御学会論文集 49 (2), 206-213, 2013
公益社団法人 計測自動制御学会
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詳細情報 詳細情報について
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- CRID
- 1390001204504322432
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- NII論文ID
- 10031154988
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- NII書誌ID
- AN00072392
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- ISSN
- 18838189
- 04534654
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- NDL書誌ID
- 024320790
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可