| 一种改进的动态数据校正离群值检测法 |
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| 引用本文： | 周凌柯,苏宏业,褚健. 一种改进的动态数据校正离群值检测法[J]. 中国化学工程学报, 2005, 13(4): 542-547 |
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| 作者姓名： | 周凌柯 苏宏业 褚健 |
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| 作者单位： | [1]Department of Automation, Nanjing University of Science ＆ Technology, Nanjing 210094, China [2]Institute of Advanced Process Control, Zhejiang University, Hangzhou 310027, Uiaxna |
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| 基金项目： | Supported by the National 0utstanding Youth Science Foundation of China （No. 60025308） and Key Technologies R＆D Program in the 10^th Five-year Plan （No. 2001BA204B07）. |
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| 摘 要： | Data reconciliation technology can decrease the level of corruption of process data due to measurement noise, but the presence of outliers caused by process peaks or unmeasured disturbances will smear the reconciled results. Based on the analysis of limitation of conventional outlier detection algorithms, a modified outlier detection method in dynamic data reconciliation （DDR） is proposed in this paper. In the modified method, the outliers of each variable are distinguished individually and the weight is modified accordingly. Therefore, the modified method can use more information of normal data, and can efficiently decrease the effect of outliers. Simulation of a continuous stirred tank reactor （CSTR） process verifies the effectiveness of the proposed algorithm.
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| 关 键 词： | 动态数据 校正方法 离群值检测法 鲁棒方法 TE问题 工业测量数据 误差 |
| 收稿时间： | 2004-04-05 |
| 修稿时间： | 2004-04-052004-11-15 |
A Modified Outlier Detection Method in Dynamic Data Reconciliation |
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| ZHOU Lingke,Su Hongye,CHU Jian. A Modified Outlier Detection Method in Dynamic Data Reconciliation[J]. Chinese Journal of Chemical Engineering, 2005, 13(4): 542-547 |
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| Authors: | ZHOU Lingke Su Hongye CHU Jian |
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| Affiliation: | Department of Automation, Nanjing University of Science & Technology, Nanjing 210094, China;Institute of Advanced Process Control, Zhejiang University, Hangzhou 310027, China;Institute of Advanced Process Control, Zhejiang University, Hangzhou 310027, China |
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| Abstract: | Data reconciliation technology can decrease the level of corruption of process data due to measurement noise, but the presence of outliers caused by process peaks or unmeasured disturbances will smear the reconciled results. Based on the analysis of limitation of conventional outlier detection algorithms, a modified outlier detection method in dynamic data reconciliation (DDR) is proposed in this paper. In the modified method, the outliers of each variable are distinguished individually and the weight is modified accordingly. Therefore, the modified method can use more information of normal data, and can efficiently decrease the effect of outliers. Simulation of a continuous stirred tank reactor (CSTR) process verifies the effectiveness of the proposed algorithm. |
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| Keywords: | data reconciliation outlier detection gross error |
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