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A soft sensing method of billet surface temperature based on ILGSSA-LSSVM

It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of the billet quality. This paper proposes a sparrow search algorithm to optimize the Least Square Support Vector Machine (LSSVM)...

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Detalles Bibliográficos
Autores principales: Liu, Jun, Yang, Luying, Nan, Xinhao, Liu, Yifan, Hou, Qingming, Lan, Kun, Yang, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763435/
https://www.ncbi.nlm.nih.gov/pubmed/36536046
http://dx.doi.org/10.1038/s41598-022-26478-3
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author Liu, Jun
Yang, Luying
Nan, Xinhao
Liu, Yifan
Hou, Qingming
Lan, Kun
Yang, Feng
author_facet Liu, Jun
Yang, Luying
Nan, Xinhao
Liu, Yifan
Hou, Qingming
Lan, Kun
Yang, Feng
author_sort Liu, Jun
collection PubMed
description It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of the billet quality. This paper proposes a sparrow search algorithm to optimize the Least Square Support Vector Machine (LSSVM) model for surface temperature prediction of the billet, which is further improved by Logistic Chaotic Mapping and Golden Sine Algorithm (Improve Logistic Golden Sine Sparrow Search Algorithm LSSVM, short name ILGSSA-LSSVM). Using the Improved Logistic Chaos Mapping and Golden Sine Algorithm to find the optimal initial sparrow population, the value of penalty factor [Formula: see text] and kernel parameter [Formula: see text] for LSSVM are calculated. Global optimization method is adopted to find the optimal parameter combination, so that the negative influence of randomly initializing parameters on the prediction accuracy would be reduced. Our proposed ILGSSA-LSSVM soft sensing model is compared respectively with traditional Least Square Support Vector Machine, BP neural network and Gray Wolf optimized Least Square Support Vector Machine, results show that proposed model outperformed the others. Experiments show that the maximum error of ILGSA-LSSVM soft sensing model is 3.85733 °C, minimum error is 0.0174 °C, average error is 0.05805 °C, and generally outperformed other comparison models.
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spelling pubmed-97634352022-12-21 A soft sensing method of billet surface temperature based on ILGSSA-LSSVM Liu, Jun Yang, Luying Nan, Xinhao Liu, Yifan Hou, Qingming Lan, Kun Yang, Feng Sci Rep Article It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of the billet quality. This paper proposes a sparrow search algorithm to optimize the Least Square Support Vector Machine (LSSVM) model for surface temperature prediction of the billet, which is further improved by Logistic Chaotic Mapping and Golden Sine Algorithm (Improve Logistic Golden Sine Sparrow Search Algorithm LSSVM, short name ILGSSA-LSSVM). Using the Improved Logistic Chaos Mapping and Golden Sine Algorithm to find the optimal initial sparrow population, the value of penalty factor [Formula: see text] and kernel parameter [Formula: see text] for LSSVM are calculated. Global optimization method is adopted to find the optimal parameter combination, so that the negative influence of randomly initializing parameters on the prediction accuracy would be reduced. Our proposed ILGSSA-LSSVM soft sensing model is compared respectively with traditional Least Square Support Vector Machine, BP neural network and Gray Wolf optimized Least Square Support Vector Machine, results show that proposed model outperformed the others. Experiments show that the maximum error of ILGSA-LSSVM soft sensing model is 3.85733 °C, minimum error is 0.0174 °C, average error is 0.05805 °C, and generally outperformed other comparison models. Nature Publishing Group UK 2022-12-19 /pmc/articles/PMC9763435/ /pubmed/36536046 http://dx.doi.org/10.1038/s41598-022-26478-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Liu, Jun
Yang, Luying
Nan, Xinhao
Liu, Yifan
Hou, Qingming
Lan, Kun
Yang, Feng
A soft sensing method of billet surface temperature based on ILGSSA-LSSVM
title A soft sensing method of billet surface temperature based on ILGSSA-LSSVM
title_full A soft sensing method of billet surface temperature based on ILGSSA-LSSVM
title_fullStr A soft sensing method of billet surface temperature based on ILGSSA-LSSVM
title_full_unstemmed A soft sensing method of billet surface temperature based on ILGSSA-LSSVM
title_short A soft sensing method of billet surface temperature based on ILGSSA-LSSVM
title_sort soft sensing method of billet surface temperature based on ilgssa-lssvm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763435/
https://www.ncbi.nlm.nih.gov/pubmed/36536046
http://dx.doi.org/10.1038/s41598-022-26478-3
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