Poor information measurement data gross error discrimination method based on Grey System Theory

Abstract

The invention provides a poor information measurement data gross error discrimination method based on the Grey System Theory, which comprises the following steps of: 1) preprocessing acquired poor information measurement data sequences and ordering the acquired poor information measurement data sequences from small to large; 2) obtaining poor information measurement data grey enveloping curves by using a grey accumulation method; 3) discriminating whether measurement data contains gross errors or not by adopting a grey discrimination rule; 4) obtaining poor information measurement data predicted values by using a grey GM (1, 1) dynamic model; and 5) repeating the step 2, the step 3 and the step 4 till the gross errors in the measurement data are all discriminated. The poor information measurement data gross error discrimination method based on the Grey System Theory can realize the effective discrimination of the gross errors in the measurement data with poor information characteristics such as unknown probability distribution and small sample size, can effectively remove the gross errors in the measurement data and can guarantee the accuracy of measurement results. The poor information measurement data gross error discrimination method based on the Grey System Theory has the advantages that the method is reasonable and simple, the calculation is simple and convenient to conduct, the calculating speed is greatly improved, and the popularization and application values in the aspect of quick and online measurement are very great.

Description

Technical field [0001] The invention belongs to the field of measurement and testing, and specifically relates to a method for judging gross errors of measurement data with lack of information based on gray theory. The method involves data modeling, data processing, gross error elimination, etc., is used for measurement data error processing, and is suitable for The data sample size is small, and the gross error of the distribution is uncertain. Background technique [0002] In the measurement process, there are inevitably errors. Gross error, also known as gross error, means that when the same value is measured multiple times under the same measurement conditions, the individual data such as the maximum or minimum value obviously deviates from the other data of the sample to which it belongs, and exceeds the expected error under the specified conditions. The measured value with gross error is called outlier, also called outlier and bad value. Gross errors may be caused by erro...

AI Technical Summary

Problems solved by technology

In this case, it is quite difficult to study the problem with the method of classical statistics

Images