Models for Prediction of Measurement Errors using Regression Analysis and Artificial Neural Networks

Abstract

Measurement is the act or the result of a quantitative comparison between a given quantity and a quantity of the same kind chosen as a unit. It is generally agreed that all measurements contain errors. In a measuring system where both a measuring instrument and a human being taking the measurement using a preset process, the measurement error could be due to the instrument, the process or the human being involved. The first part of the study is devoted to understanding the human errors in measurement. The second part of this research work concentrated on the characterization of errors observed during calibration done periodically (effect of time) of selected sophisticated instruments and selected standards used in legal metrology. The extent of errors due to passage of time and use, were found for some sophisticated instruments and some standards used in legal metrology. These studies have enabled the researcher to characterize errors in these instruments and thus add to the understanding of measurement errors. In order to make the data collected more useful, Regression and Artificial Neural Network [ANN] based models have been developed to predict error [extent] for instrument type and standard types studied.