DYNAMIC MEASUREMENTS ERROR CORRECTION ON A BASIS OF NEURAL NETWORK INVERSE MODEL OF A SENSOR

Andrei S. Volosnikov, Aleksandr L. Shestakov

Abstract

The neural network inverse model of a sensor with the filtration of the sequentially recovered signal is considered. This model allows it to effectively correct the dynamic measurements error due to the deep mathematical processing of measurement data. The result of the experimental data processing of the dynamic temperature measurements validates the efficiency of the proposed model and the algorithm of the dynamic measurements error correction.

Keywords

dynamic measurements error, neural network model, inverse sensor model, recovery of sensor input signal, dynamic measurements data processing

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IMEKO-WC-2015-TC21-421.pdf

DOI

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IMEKO TC

TC21 - Mathematical Tools for Measurements

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