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Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique

The paper presents two methods of dynamic error correction applied to transducers used for the measurement of gas concentration. One of them is based on a parametric model of the transducer dynamics, and the second one uses the artificial neural network (ANN) technique. This article describes resear...

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Detalles Bibliográficos
Autor principal: Roj, Jerzy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017432/
https://www.ncbi.nlm.nih.gov/pubmed/27517933
http://dx.doi.org/10.3390/s16081267
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author Roj, Jerzy
author_facet Roj, Jerzy
author_sort Roj, Jerzy
collection PubMed
description The paper presents two methods of dynamic error correction applied to transducers used for the measurement of gas concentration. One of them is based on a parametric model of the transducer dynamics, and the second one uses the artificial neural network (ANN) technique. This article describes research of the dynamic properties of the gas concentration measuring transducer with a typical sensor based on tin dioxide. Its response time is about 8 min, which may be not acceptable in many applications. On the basis of these studies, a parametric model of the transducer dynamics and an adequate correction algorithm has been developed. The results obtained in the research of the transducer were also used for learning and testing ANN, which were implemented in the dynamic correction task. Despite the simplicity of the used models, both methods allowed a significant reduction of the transducer’s response time. For the algorithm based on the parametric model the response time was shorter by approximately eight-fold (reduced up to 40–80 s, i.e., about 2–4 sample periods), whereas with the use of an ANN the output signal was practically fixed after a time equal to one sampling period, i.e., 20 s. In addition, the use of ANN has allowed reducing the impact of the transducer dynamic non-linearity on the correction effectiveness.
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spelling pubmed-50174322016-09-22 Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique Roj, Jerzy Sensors (Basel) Article The paper presents two methods of dynamic error correction applied to transducers used for the measurement of gas concentration. One of them is based on a parametric model of the transducer dynamics, and the second one uses the artificial neural network (ANN) technique. This article describes research of the dynamic properties of the gas concentration measuring transducer with a typical sensor based on tin dioxide. Its response time is about 8 min, which may be not acceptable in many applications. On the basis of these studies, a parametric model of the transducer dynamics and an adequate correction algorithm has been developed. The results obtained in the research of the transducer were also used for learning and testing ANN, which were implemented in the dynamic correction task. Despite the simplicity of the used models, both methods allowed a significant reduction of the transducer’s response time. For the algorithm based on the parametric model the response time was shorter by approximately eight-fold (reduced up to 40–80 s, i.e., about 2–4 sample periods), whereas with the use of an ANN the output signal was practically fixed after a time equal to one sampling period, i.e., 20 s. In addition, the use of ANN has allowed reducing the impact of the transducer dynamic non-linearity on the correction effectiveness. MDPI 2016-08-10 /pmc/articles/PMC5017432/ /pubmed/27517933 http://dx.doi.org/10.3390/s16081267 Text en © 2016 by the author; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Roj, Jerzy
Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique
title Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique
title_full Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique
title_fullStr Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique
title_full_unstemmed Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique
title_short Correction of Dynamic Errors of a Gas Sensor Based on a Parametric Method and a Neural Network Technique
title_sort correction of dynamic errors of a gas sensor based on a parametric method and a neural network technique
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017432/
https://www.ncbi.nlm.nih.gov/pubmed/27517933
http://dx.doi.org/10.3390/s16081267
work_keys_str_mv AT rojjerzy correctionofdynamicerrorsofagassensorbasedonaparametricmethodandaneuralnetworktechnique