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Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network

After using a catalytic-combustion-based combustible-gas sensor (catalytic sensor) underground for a period of time, the sensitivity drifts due to environmental factors such as coal dust, temperature, and humidity. It is necessary to adjust the sensor regularly to ensure its accuracy. In this paper,...

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Detalles Bibliográficos
Autor principal: Bowen, Wang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006743/
https://www.ncbi.nlm.nih.gov/pubmed/36915543
http://dx.doi.org/10.1016/j.heliyon.2023.e14055
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author Bowen, Wang
author_facet Bowen, Wang
author_sort Bowen, Wang
collection PubMed
description After using a catalytic-combustion-based combustible-gas sensor (catalytic sensor) underground for a period of time, the sensitivity drifts due to environmental factors such as coal dust, temperature, and humidity. It is necessary to adjust the sensor regularly to ensure its accuracy. In this paper, RBF neural network technology is introduced to fit a nonlinear continuous function to solve the problem of the output error of the sensor being too large due to linear adjustment. Through experimental analysis, it is demonstrated that the RBF neural network model has a higher convergence speed and smaller error than other network models. By embedding the RBF network model into a sensor microcontroller, the error of traditional linear calibration can be reduced by two orders of magnitude and the measurement accuracy of the catalytic sensor can be greatly improved.
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spelling pubmed-100067432023-03-12 Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network Bowen, Wang Heliyon Research Article After using a catalytic-combustion-based combustible-gas sensor (catalytic sensor) underground for a period of time, the sensitivity drifts due to environmental factors such as coal dust, temperature, and humidity. It is necessary to adjust the sensor regularly to ensure its accuracy. In this paper, RBF neural network technology is introduced to fit a nonlinear continuous function to solve the problem of the output error of the sensor being too large due to linear adjustment. Through experimental analysis, it is demonstrated that the RBF neural network model has a higher convergence speed and smaller error than other network models. By embedding the RBF network model into a sensor microcontroller, the error of traditional linear calibration can be reduced by two orders of magnitude and the measurement accuracy of the catalytic sensor can be greatly improved. Elsevier 2023-02-28 /pmc/articles/PMC10006743/ /pubmed/36915543 http://dx.doi.org/10.1016/j.heliyon.2023.e14055 Text en © 2023 The Author. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Bowen, Wang
Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network
title Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network
title_full Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network
title_fullStr Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network
title_full_unstemmed Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network
title_short Research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on RBF neural network
title_sort research on nonlinear calibration of mine catalytic-combustion-based combustible-gas sensor based on rbf neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006743/
https://www.ncbi.nlm.nih.gov/pubmed/36915543
http://dx.doi.org/10.1016/j.heliyon.2023.e14055
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