Abstract
This study is focused on the application of the forecast results obtained with the help of a dual-parametric neural network for measuring instruments calibration within a limited range. In this report describing our study we intend to give a theoretical overview of the previous research on the subject. We also consider the learning algorithms for interval neural networks with hidden layer weights assigned in either real or interval values. There is a description of a dual-parametric neural network as a neural network type and the examination of learning algorithms for the respective interval neural network models. We consider the calibration methodology and the calculation procedure to determine the applicability of forecasts based on the use of interval neural networks. There is a presentation of the results of numeric experiments aimed at the dosimeter readings predicting by means of the dual-parametric neuron network. Finally, we analyze the results obtained while drawing conclusions regarding the applicability of this approach for measuring instruments calibration.
Published Version
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