| 一种基于F统计量的改进型显著误差检测方法 |
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| 引用本文： | 吴胜昔,彭竹,沈凯,顾幸生.一种基于F统计量的改进型显著误差检测方法[J].医学教育探索,2014(2):206-211. |
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| 作者姓名： | 吴胜昔 彭竹 沈凯 顾幸生 |
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| 作者单位： | 华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海 200237;华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海 200237;华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海 200237;华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海 200237 |
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| 基金项目： | 国家自然科学基金(61174040)；上海市科委基础研究项目（12JC1403400）；上海市重点学科建设项目（B504） |
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| 摘 要： | 工业过程采集的数据的可靠性和准确度直接影响到过程控制、调度及优化等。讨论了数据校正的原理及应用，分析了显著误差检测的意义以及显著误差检测的基本原理。在对两种传统的基于统计量的显著误差检测法讨论的基础上，提出了一种基于F统计量的NT MT显著误差检测方法。该方法将两种传统方法运用其中，仿真结果表明，基于F统计量的改进NT MT方法给出了很好的检测效果，对显著误差的灵敏度很高。
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| 关 键 词： | 数据协调 显著误差 统计量 协方差 |
| 收稿时间： | 2013/8/15 0:00:00 |
A Modified Gross Error Detection Method Based on F Statistic |
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| WU Sheng-xi,PENG Zhu,SHEN Kai and GU Xing-sheng.A Modified Gross Error Detection Method Based on F Statistic[J].Researches in Medical Education,2014(2):206-211. |
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| Authors: | WU Sheng-xi PENG Zhu SHEN Kai and GU Xing-sheng |
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| Institution: | Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China |
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| Abstract: | The reliability and accuracy of industrial process data directly affect the process control, scheduling and optimization. Thus, the measurement data with high accuracy, reliability and consistency are quite important. This paper discusses the theory and application of data rectification, and analyzes the significance of gross error detection method. By investing two kinds of traditional statistics based gross error detection methods, this paper proposes an F statistic based NT MT detection method. Compared with two traditional methods, the improved NT MT algorithm can attain better detection results and has higher sensitiveness to gross error. |
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| Keywords: | data reconciliation gross error statistics covariance |
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