A Method for Discriminating Gross Errors of Measuring Data with Insufficient Information Based on Gray Theory
A technology for measuring data and gross errors, applied in complex mathematical operations and other directions, and can solve problems such as classical statistical difficulties
Inactive Publication Date: 2015-09-09
BEIHANG UNIV
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In this case, it is quite difficult to study the problem with the method of classical statistics
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[0051] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
[0052] The present invention proposes a coarse error discrimination method based on gray accumulation and gray GM (1,1) dynamic model for measurement data with little data and unknown probability distribution.
[0053] one sight figure 1 , the present invention is based on gray theory lack of information measurement data coarse error discrimination method, it comprises the following steps:
[0054] Step 1: First sort the n measurement data from small to large, the sequence is:
[0055] x (0) ={x (0) (1),x (0) (2),...,x (0) (n)} (14)
[0056] Step 2: For x (0) Accumulate and generate, and get the measured value accumulative sequence x (1)
[0057] First sort the n measurement data from small to large, the sequence is:
[0058] x (0) ={x (0) (1),x (0) (2),...,x (0) (n),...} (15)
[0059] to x (0) Accumulate and generate, and get the...
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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 metrology and testing, and in particular relates to a method for discriminating gross errors in measurement data lacking information based on gray theory. The method involves data modeling, data processing, gross error elimination, etc., and is used for measuring data error processing, and is applicable to Gross error discrimination with small data sample size and uncertain distribution. Background technique [0002] During the measurement process, errors are inevitable. Gross error, also known as gross error, refers to when the same value is measured multiple times under the same measurement conditions, the individual data such as the maximum or minimum value obviously deviate from other data of the sample to which it belongs, exceeding the error expected under the specified conditions. Measurements that contain gross errors are called outliers, also known as outliers and bad values. Gross errors may be caused b...
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IPC IPC(8): G06F17/10
Inventor 王中宇王倩王岩庆李强
Owner BEIHANG UNIV