Temperature compensation based on BP neural network with small sample data for chloride ions optical fiber probe
•The small sample data temperature compensation of chloride ion probe was proposed.•An improved adaptive BP neural network algorithm was designed.•The Black Widow Optimization Algorithm was combined with BP neural network.•The improved model achieves good recognition accuracy (1.21% relative error)....
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| Published in | Optics and laser technology Vol. 176; p. 110973 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.09.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0030-3992 |
| DOI | 10.1016/j.optlastec.2024.110973 |
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| Abstract | •The small sample data temperature compensation of chloride ion probe was proposed.•An improved adaptive BP neural network algorithm was designed.•The Black Widow Optimization Algorithm was combined with BP neural network.•The improved model achieves good recognition accuracy (1.21% relative error).
Fiber optic sensors have great applied in the field of sensing, however they are subject to temperature. In this study, we proposed an improved small sample data Back Propagating (BP) neural network for temperature compensation of a chloride ion probe based on an optical fiber Fabry-Perot interferometer (FPI). The temperature compensation results show that the Black Widow Optimization (BWO) algorithm was combined with BP neural network to further improve the performance of the model with a great detection accuracy that the relative error is 1.21%, associated with a Mean Square Error (MSE) of 2.6e−5.This is an excellent temperature compensation method with low computational cost and small samples. |
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| AbstractList | •The small sample data temperature compensation of chloride ion probe was proposed.•An improved adaptive BP neural network algorithm was designed.•The Black Widow Optimization Algorithm was combined with BP neural network.•The improved model achieves good recognition accuracy (1.21% relative error).
Fiber optic sensors have great applied in the field of sensing, however they are subject to temperature. In this study, we proposed an improved small sample data Back Propagating (BP) neural network for temperature compensation of a chloride ion probe based on an optical fiber Fabry-Perot interferometer (FPI). The temperature compensation results show that the Black Widow Optimization (BWO) algorithm was combined with BP neural network to further improve the performance of the model with a great detection accuracy that the relative error is 1.21%, associated with a Mean Square Error (MSE) of 2.6e−5.This is an excellent temperature compensation method with low computational cost and small samples. |
| ArticleNumber | 110973 |
| Author | Li, Yu Jin, Wa Fu, Guangwei Fu, Xinghu Li, Xia Ke, Sicheng Bi, Weihong |
| Author_xml | – sequence: 1 givenname: Xia surname: Li fullname: Li, Xia – sequence: 2 givenname: Sicheng surname: Ke fullname: Ke, Sicheng – sequence: 3 givenname: Yu surname: Li fullname: Li, Yu – sequence: 4 givenname: Wa surname: Jin fullname: Jin, Wa – sequence: 5 givenname: Xinghu surname: Fu fullname: Fu, Xinghu – sequence: 6 givenname: Guangwei surname: Fu fullname: Fu, Guangwei – sequence: 7 givenname: Weihong surname: Bi fullname: Bi, Weihong email: bwhong@ysu.edu.cn |
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| Cites_doi | 10.1007/978-981-10-6373-2_14 10.1080/10739149.2013.816965 10.3390/electronics8040425 10.1364/OE.463396 10.1016/j.saa.2020.118169 10.1080/01431161.2021.1910367 10.1088/1755-1315/117/1/012031 10.1016/j.jiec.2020.09.020 10.1364/OE.19.020003 10.1016/j.jcf.2008.07.005 10.1016/j.engappai.2019.103249 10.1016/j.atmosenv.2016.10.024 10.1021/acs.est.6b00679 10.1007/s11356-021-14065-4 10.1016/j.snb.2021.131134 10.1016/j.econmod.2020.06.008 10.1016/j.heliyon.2023.e20133 10.3390/horticulturae8030261 |
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| References | Peng, Zhou, Liu, He, Gao, Guo (b0050) 2020; 233 Xu, Song, Xia, Chen, Wang, Wei (b0055) 2017; 762 I. Goodfellow, Y. Bengio, A. Courville, Y. Bengio, Deep Learning, Cambridge, 2016. Hayyolalam, Kazem (b0090) 2020; 87 Zhu, Jiang, Chen, Wang, Sun, Zhang, Wang, Huang (b0025) 2022; 30 Zhao, Zhao, Wang, Peng, Hu (b0030) 2022; 353 Li, Li, Li (b0080) 2015; 14 Kaushal (b0010) 2016; 50 Suresh, Rajan, Pushparaj (b0100) 2021; 42 Popoola, Stewart, Mead, Jones (b0040) 2016; 147 Panahi, Ehteram, Emami (b0095) 2021; 28 Adrián (b0110) 2020; 8 Wu, Guan, Lu, Tam (b0035) 2011; 19 Wang, Zhang, You, Yuan, Zhao, Jiang (b0075) 2013; 41 Alweshah, Aldabbas, Abu-Salih (b0105) 2023; 9 Li, Zhang (b0045) 2017 Xu, Feng, Xing (b0085) 2019; 8 Toscano, La Fornara, Romano (b0015) 2022; 8 Lebecque, Leonard, De Boeck, De Baets, Malfroot, Casimir, Desager, Godding, Leal (b0020) 2009; 8 Abidin, Asmat, Hamidon (b0060) 2018; 117 Jahn (b0065) 2020; 91 Wang, Du, Yang, Tian, Ge, Huang, Wang (b0005) 2021; 93 Zhu (10.1016/j.optlastec.2024.110973_b0025) 2022; 30 Panahi (10.1016/j.optlastec.2024.110973_b0095) 2021; 28 Xu (10.1016/j.optlastec.2024.110973_b0085) 2019; 8 Peng (10.1016/j.optlastec.2024.110973_b0050) 2020; 233 Abidin (10.1016/j.optlastec.2024.110973_b0060) 2018; 117 Adrián (10.1016/j.optlastec.2024.110973_b0110) 2020; 8 Xu (10.1016/j.optlastec.2024.110973_b0055) 2017; 762 10.1016/j.optlastec.2024.110973_b0070 Wu (10.1016/j.optlastec.2024.110973_b0035) 2011; 19 Popoola (10.1016/j.optlastec.2024.110973_b0040) 2016; 147 Zhao (10.1016/j.optlastec.2024.110973_b0030) 2022; 353 Hayyolalam (10.1016/j.optlastec.2024.110973_b0090) 2020; 87 Li (10.1016/j.optlastec.2024.110973_b0080) 2015; 14 Toscano (10.1016/j.optlastec.2024.110973_b0015) 2022; 8 Kaushal (10.1016/j.optlastec.2024.110973_b0010) 2016; 50 Li (10.1016/j.optlastec.2024.110973_b0045) 2017 Suresh (10.1016/j.optlastec.2024.110973_b0100) 2021; 42 Lebecque (10.1016/j.optlastec.2024.110973_b0020) 2009; 8 Alweshah (10.1016/j.optlastec.2024.110973_b0105) 2023; 9 Jahn (10.1016/j.optlastec.2024.110973_b0065) 2020; 91 Wang (10.1016/j.optlastec.2024.110973_b0075) 2013; 41 Wang (10.1016/j.optlastec.2024.110973_b0005) 2021; 93 |
| References_xml | – volume: 30 start-page: 34956 year: 2022 end-page: 34972 ident: b0025 article-title: Highly sensitive gas pressure sensor based on the enhanced Vernier effect through a cascaded fabry-perot and mach-zehnder interferometer publication-title: Opt. Express – volume: 762 start-page: 135 year: 2017 end-page: 145 ident: b0055 article-title: Temperature and humidity compensation for MOS gas sensor based on random forests publication-title: Intelligent Computing, Networked Control, and Their Engineering Applications – volume: 28 year: 2021 ident: b0095 article-title: Suspended sediment load prediction based on soft computing models and black Widow optimization algorithm using an enhanced gamma test publication-title: Environ. Sci. Pollut. Res. – volume: 353 start-page: 10 year: 2022 ident: b0030 article-title: Femtosecond laser-inscribed fiber-optic sensor for seawater salinity and temperature measurements publication-title: Sens. Actuator B-Chem. – volume: 8 start-page: 26 year: 2009 end-page: 30 ident: b0020 article-title: Early referral to cystic fibrosis specialist centre impacts on respiratory outcome publication-title: J. Cyst. Fibros – volume: 117 year: 2018 ident: b0060 article-title: Comparative study of drift compensation methods for environmental gas sensors publication-title: IOP Conf. Ser. Earth Environ. Sci. – volume: 42 year: 2021 ident: b0100 article-title: Dehazing of satellite images using Adaptive black Widow optimization-based framework publication-title: Int. J. Remote Sens. – volume: 93 start-page: 170 year: 2021 end-page: 175 ident: b0005 article-title: Removal of chloride ions from acidic solution with antimony oxides publication-title: J. Ind. Eng. Chem. – volume: 8 start-page: 1 year: 2020 end-page: 10 ident: b0110 article-title: Pea-delgado, hernán Peraza-vázquez, Juan H, Almazán-Covarrubias, a novel bio-inspired algorithm applied to selective Harmonic elimination in a three-phase eleven-level inverter publication-title: Math. Probl. Eng. – volume: 87 year: 2020 ident: b0090 article-title: Black Widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems publication-title: Eng. Appl. Artif. Intell. – volume: 233 year: 2020 ident: b0050 article-title: An ultra-sensitive detection system for sulfur dioxide and nitric oxide based on improved differential optical absorption spectroscopy method publication-title: Spectrochim. Acta A Mol. Biomol. Spectrosc. – volume: 91 start-page: 148 year: 2020 end-page: 154 ident: b0065 article-title: Artificial neural network regression models in a panel setting: predicting economic growth publication-title: Econ. Model. – volume: 8 start-page: 26 year: 2022 ident: b0015 article-title: Salt spray and Surfactants induced morphological, physiological, and biochemical responses in Callistemon citrinus (Curtis) plants publication-title: Horticulturae – volume: 147 start-page: 330 year: 2016 end-page: 343 ident: b0040 article-title: Development of a baseline temperature correction methodology for electrochemical sensors and its implications for long-term stability publication-title: Atmos. Environ. – volume: 19 start-page: 6 year: 2011 ident: b0035 article-title: Salinity sensor based on polyimide-coated photonic crystal fiber publication-title: Opt. Express – start-page: 99 year: 2017 end-page: 103 ident: b0045 article-title: Research on key technology of CO2 concentration measurement based on non-dispersive infrared, in publication-title: 2nd International Conference on Frontiers of Sensors Technologies (ICFST) – reference: I. Goodfellow, Y. Bengio, A. Courville, Y. Bengio, Deep Learning, Cambridge, 2016. – volume: 50 start-page: 2765 year: 2016 end-page: 2766 ident: b0010 article-title: Increased salinization decreases safe drinking water publication-title: Environ. Sci. Technol. – volume: 8 year: 2019 ident: b0085 article-title: Modeling and analysis of Adaptive temperature compensation for humidity sensors publication-title: Electronics – volume: 9 year: 2023 ident: b0105 article-title: Hybrid black widow optimization with iterated greedy algorithm for gene selection problems publication-title: Heliyon – volume: 14 start-page: 1 year: 2015 end-page: 6 ident: b0080 article-title: The Research of temperature compensation for thermopile sensor based on improved PSO-BP algorithm publication-title: Math. Probl. Eng. – volume: 41 start-page: 608 year: 2013 end-page: 618 ident: b0075 article-title: Back propagation neural network model for temperature and humidity compensation of a non dispersive infrared methane sensor publication-title: Instrum. Sci. Technol. – volume: 762 start-page: 135 year: 2017 ident: 10.1016/j.optlastec.2024.110973_b0055 article-title: Temperature and humidity compensation for MOS gas sensor based on random forests publication-title: Intelligent Computing, Networked Control, and Their Engineering Applications doi: 10.1007/978-981-10-6373-2_14 – volume: 41 start-page: 608 year: 2013 ident: 10.1016/j.optlastec.2024.110973_b0075 article-title: Back propagation neural network model for temperature and humidity compensation of a non dispersive infrared methane sensor publication-title: Instrum. Sci. Technol. doi: 10.1080/10739149.2013.816965 – volume: 8 year: 2019 ident: 10.1016/j.optlastec.2024.110973_b0085 article-title: Modeling and analysis of Adaptive temperature compensation for humidity sensors publication-title: Electronics doi: 10.3390/electronics8040425 – ident: 10.1016/j.optlastec.2024.110973_b0070 – volume: 30 start-page: 34956 year: 2022 ident: 10.1016/j.optlastec.2024.110973_b0025 article-title: Highly sensitive gas pressure sensor based on the enhanced Vernier effect through a cascaded fabry-perot and mach-zehnder interferometer publication-title: Opt. Express doi: 10.1364/OE.463396 – volume: 233 year: 2020 ident: 10.1016/j.optlastec.2024.110973_b0050 article-title: An ultra-sensitive detection system for sulfur dioxide and nitric oxide based on improved differential optical absorption spectroscopy method publication-title: Spectrochim. Acta A Mol. Biomol. Spectrosc. doi: 10.1016/j.saa.2020.118169 – volume: 42 year: 2021 ident: 10.1016/j.optlastec.2024.110973_b0100 article-title: Dehazing of satellite images using Adaptive black Widow optimization-based framework publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2021.1910367 – volume: 117 year: 2018 ident: 10.1016/j.optlastec.2024.110973_b0060 article-title: Comparative study of drift compensation methods for environmental gas sensors publication-title: IOP Conf. Ser. Earth Environ. Sci. doi: 10.1088/1755-1315/117/1/012031 – volume: 93 start-page: 170 issue: 2020 year: 2021 ident: 10.1016/j.optlastec.2024.110973_b0005 article-title: Removal of chloride ions from acidic solution with antimony oxides publication-title: J. Ind. Eng. Chem. doi: 10.1016/j.jiec.2020.09.020 – volume: 19 start-page: 6 year: 2011 ident: 10.1016/j.optlastec.2024.110973_b0035 article-title: Salinity sensor based on polyimide-coated photonic crystal fiber publication-title: Opt. Express doi: 10.1364/OE.19.020003 – volume: 8 start-page: 26 year: 2009 ident: 10.1016/j.optlastec.2024.110973_b0020 article-title: Early referral to cystic fibrosis specialist centre impacts on respiratory outcome publication-title: J. Cyst. Fibros doi: 10.1016/j.jcf.2008.07.005 – volume: 87 year: 2020 ident: 10.1016/j.optlastec.2024.110973_b0090 article-title: Black Widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2019.103249 – volume: 147 start-page: 330 year: 2016 ident: 10.1016/j.optlastec.2024.110973_b0040 article-title: Development of a baseline temperature correction methodology for electrochemical sensors and its implications for long-term stability publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2016.10.024 – volume: 50 start-page: 2765 year: 2016 ident: 10.1016/j.optlastec.2024.110973_b0010 article-title: Increased salinization decreases safe drinking water publication-title: Environ. Sci. Technol. doi: 10.1021/acs.est.6b00679 – start-page: 99 year: 2017 ident: 10.1016/j.optlastec.2024.110973_b0045 article-title: Research on key technology of CO2 concentration measurement based on non-dispersive infrared, in – volume: 28 year: 2021 ident: 10.1016/j.optlastec.2024.110973_b0095 article-title: Suspended sediment load prediction based on soft computing models and black Widow optimization algorithm using an enhanced gamma test publication-title: Environ. Sci. Pollut. Res. doi: 10.1007/s11356-021-14065-4 – volume: 353 start-page: 10 year: 2022 ident: 10.1016/j.optlastec.2024.110973_b0030 article-title: Femtosecond laser-inscribed fiber-optic sensor for seawater salinity and temperature measurements publication-title: Sens. Actuator B-Chem. doi: 10.1016/j.snb.2021.131134 – volume: 91 start-page: 148 year: 2020 ident: 10.1016/j.optlastec.2024.110973_b0065 article-title: Artificial neural network regression models in a panel setting: predicting economic growth publication-title: Econ. Model. doi: 10.1016/j.econmod.2020.06.008 – volume: 14 start-page: 1 year: 2015 ident: 10.1016/j.optlastec.2024.110973_b0080 article-title: The Research of temperature compensation for thermopile sensor based on improved PSO-BP algorithm publication-title: Math. Probl. Eng. – volume: 9 year: 2023 ident: 10.1016/j.optlastec.2024.110973_b0105 article-title: Hybrid black widow optimization with iterated greedy algorithm for gene selection problems publication-title: Heliyon doi: 10.1016/j.heliyon.2023.e20133 – volume: 8 start-page: 26 year: 2022 ident: 10.1016/j.optlastec.2024.110973_b0015 article-title: Salt spray and Surfactants induced morphological, physiological, and biochemical responses in Callistemon citrinus (Curtis) plants publication-title: Horticulturae doi: 10.3390/horticulturae8030261 – volume: 8 start-page: 1 year: 2020 ident: 10.1016/j.optlastec.2024.110973_b0110 article-title: Pea-delgado, hernán Peraza-vázquez, Juan H, Almazán-Covarrubias, a novel bio-inspired algorithm applied to selective Harmonic elimination in a three-phase eleven-level inverter publication-title: Math. Probl. Eng. |
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| Title | Temperature compensation based on BP neural network with small sample data for chloride ions optical fiber probe |
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