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Real-time rejecting method for observed experimental data outliers

A technology of experimental data and outliers, applied in the field of experimental data analysis, can solve the problems of experimental data usage restrictions, static processing methods cannot be judged in real time, etc., and achieve a wide range of applications

Active Publication Date: 2018-06-26
HIWING TECH ACAD OF CASIC
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Problems solved by technology

[0006] The purpose of the present invention is to provide a real-time removal method for abnormal values ​​in observed experimental data, to solve the problem that the static processing method cannot judge the experimental data in real time and the problem of the use limitation of the currently known real-time removal method

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  • Real-time rejecting method for observed experimental data outliers
  • Real-time rejecting method for observed experimental data outliers

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[0054] First, make statistics on the historical experimental data of the variable set X, take the absolute value of the difference between the experimental data of the two points before and after, and arrange the results in ascending order; then determine an abnormal value detection level, such as 3‰; then according to the abnormal value The value detection level calculates the arrangement order of the outlier threshold value in the order statistics; thus the outlier threshold value Y(m) corresponding to the variable set X can be determined.

[0055] After determining the outlier threshold, any set of experimental data {x i} for dynamic outlier processing, first, if the experimental data x i is the first observation point of this experiment, that is, i=1 is established, then x i It is judged as a normal value; if x i If it is not the first observation point, then judge whether the absolute value of the difference between the experimental data currently observed and the exper...

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Abstract

The invention provides a real-time rejecting method for observed experimental data outliers. The method comprises following steps: performing statistics on existing experimental data of a certain variable set X to determine an outlier threshold; performing real-time detection and rejecting on outliers of experimental data according to a given engineering mathematical model according to the outlierthreshold. The invention determines the outlier threshold by counting the historical experimental data of the continuously changing variable to be observed. The dynamic processing mathematical modelis used to dynamically judge the current experimental data observed by the experimental system, and the outliers can be judged and the outliers can be eliminated, which helps to more realistically evaluate, predict or restore the real variable to be observed. The method provided by the invention is not limited by the use conditions and has a wider application range, and provides an effective dynamic processing method for observing outliers in experimental data.

Description

technical field [0001] The invention belongs to the technical field of experimental data analysis, and relates to a method for eliminating abnormal values ​​in observed experimental data in real time, in particular to a method for eliminating abnormal values ​​in observed experimental data with continuous variation characteristics. Background technique [0002] Most of the existing experimental data outlier processing methods are static processing methods, such as "Statistical Analysis of Experimental Data" (Chinese People's Liberation Army General Armament Department Military Training Textbook Editing Committee January 2001 National Defense Industry Press ISBN 7-118-02370- 1 P67~P77) use extreme value deviation method, range ratio method and other methods to deal with outliers (or outliers, outliers), and judge the group of experimental data by sorting a group of experimental data belonging to the same variable set Whether the larger or smaller data in the data deviate sign...

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Application Information

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IPC IPC(8): G06F7/24G06F17/18
CPCG06F7/24G06F17/18
Inventor 刘忠诚曹薇薇
Owner HIWING TECH ACAD OF CASIC
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