Neural network approach to reduce dynamic measurement errors

Authors

  • Andrei S. Volosnikov South Ural State University
  • Aleksandr L. Shestakov South Ural State University

DOI:

https://doi.org/10.21014/acta_imeko.v5i3.294

Abstract

The neural network inverse model of a sensor with filtration of the sequentially recovered signal is considered. This model effectively reduces the dynamic measurement errors due to deep mathematical processing of measurement data. The result of the experimental data processing of a dynamic temperature measurement validates the efficiency of the proposed neural network approach to reduce dynamic measurement errors.

Author Biography

Andrei S. Volosnikov, South Ural State University

Department of Information-Measuring Engineering

Faculty of Computer Technologies, Control and Radio Electronics

Associate professor

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Published

2016-11-04

Issue

Vol. 5 No. 3 (2016)

Section

Research Papers

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