Methods and models of neural networks for approximation of calibration characteristics of NTC-thermistors




accuracy, measuring data, calibration, NTC-thermistor, operating temperature range, transformation function, neural network approximation, RBF-network


The hypothesis about the expediency of using RBF-networks to improve the accuracy of constructing the calibration characteristics of NTC-thermistors in the operating temperature range without dividing it into subranges is confirmed. It has been established that the error of the neural network approximation of the calibration characteristics of NTC-thermistors based on RBF-networks is at least one and a half times less than the permissible error of approximation of the third-order polynomial model, which is used in the software of modern systems for collecting and processing measurement information. A technique has been developed for processing measurement information using adaptive RBF-networks to automate constructing individual calibration characteristics and periodic calibration of NTC-thermistors.

Author Biographies

Serhii Fedin, National Transport University, Kyiv

Doctor of Technical Sciences, a professor at the Department of Information Systems and Technologies of the National Transport University, Kyiv, Ukraine.

Irina Zubretska, “Trade City”, Kyiv

Information technology specialist at “Trade City”, Kyiv, Ukraine.


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Theoretical and applied problems of intelligent systems for decision making support