Computer simulation of multiple measurements of logarithmic transformation function by two approaches
DOI:
https://doi.org/10.15587/1729-4061.2020.218517Keywords:
redundant methods, multiple measurements, measurement equation, function parameters, accuracy increaseAbstract
The studies of the capabilities of redundant measurement methods revealed the high efficiency of the presented methods in increasing the accuracy of multiple measurements. It was proved that redundant measurement equations ensure the independence of the measurement result from the parameters of the transformation function and their deviations from the nominal values. Experimental studies have confirmed that the accuracy of multiple measurements is increased by processing the results of intermediate measurements using equations of redundant measurements by two approaches. In particular, it was found that processing the results of multiple measurements with the logarithmic transformation function with the first approach provides the value of the relative error of 0.75×10 %, and the second – 0.02×10.
This suggests that the increase in accuracy is due to the total effect of the elimination of the systematic error component due to changes in the parameters of the transformation function and reduction of the random error component. The latter, in particular, concerns the algorithms for processing multiple measurements by two approaches. A comparative analysis was made, the advantages and disadvantages of each of the two approaches were determined. It was found that the second approach is less sensitive to an increase in the difference between the values of the controlled and normalized quantities. This allows us to state the possibility of measuring the controlled parameter of a large value without imposing high requirements on the power of the calibrated radiation source.
There is reason to assert about the promising development of redundant measurement methods in the processing of the results of multiple measurements in the field of increasing accuracy with the nonlinear transformation function
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