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A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM paramet...

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Detalles Bibliográficos
Autores principales: Jiang, Minlan, Jiang, Lan, Jiang, Dingde, Li, Fei, Song, Houbing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5796361/
https://www.ncbi.nlm.nih.gov/pubmed/29342942
http://dx.doi.org/10.3390/s18010233
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author Jiang, Minlan
Jiang, Lan
Jiang, Dingde
Li, Fei
Song, Houbing
author_facet Jiang, Minlan
Jiang, Lan
Jiang, Dingde
Li, Fei
Song, Houbing
author_sort Jiang, Minlan
collection PubMed
description Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.
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spelling pubmed-57963612018-02-13 A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM Jiang, Minlan Jiang, Lan Jiang, Dingde Li, Fei Song, Houbing Sensors (Basel) Article Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. MDPI 2018-01-15 /pmc/articles/PMC5796361/ /pubmed/29342942 http://dx.doi.org/10.3390/s18010233 Text en © 2018 by the authors. 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
Jiang, Minlan
Jiang, Lan
Jiang, Dingde
Li, Fei
Song, Houbing
A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM
title A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM
title_full A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM
title_fullStr A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM
title_full_unstemmed A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM
title_short A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM
title_sort sensor dynamic measurement error prediction model based on napso-svm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5796361/
https://www.ncbi.nlm.nih.gov/pubmed/29342942
http://dx.doi.org/10.3390/s18010233
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