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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-5796361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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