The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm

Traditional atmospheric models are based on the analysis and fitting of various factors influencing the space atmosphere density. Neural network models do not specifically analyze the polynomials of each influencing factor in the atmospheric model, but use large data sets for network construction. T...

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Autores principales:	Cui Wenhui, Qu Wei, Jiang Min, Yao Gang
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	improved levenberg-marquardt algorithm neural networks atmospheric model Astronomy QB1-991
Acceso en línea:	https://doaj.org/article/73725b7e13e84cff8a06a8201f0566ca
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id	oai:doaj.org-article:73725b7e13e84cff8a06a8201f0566ca
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spelling	oai:doaj.org-article:73725b7e13e84cff8a06a8201f0566ca2021-12-05T14:10:40ZThe atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm2543-637610.1515/astro-2021-0003https://doaj.org/article/73725b7e13e84cff8a06a8201f0566ca2021-08-01T00:00:00Zhttps://doi.org/10.1515/astro-2021-0003https://doaj.org/toc/2543-6376Traditional atmospheric models are based on the analysis and fitting of various factors influencing the space atmosphere density. Neural network models do not specifically analyze the polynomials of each influencing factor in the atmospheric model, but use large data sets for network construction. Two traditional atmospheric model algorithms are analyzed, the main factors affecting the atmospheric model are identified, and an atmospheric model based on neural networks containing various influencing factors is proposed. According to the simulation error, the Levenberg-Marquardt algorithm is used to iteratively realize the rapid network weight correction, and the optimal neural network atmospheric model is obtained. The space atmosphere is simulated and calculated with an atmospheric model based on neural networks, and its average error rate is lower than that of traditional atmospheric models such as the DTM2013 model and the MSIS00 model. At the same time, the calculation complexity of the atmospheric model based on the neural networks is significantly simplified than that of the traditional atmospheric model.Cui WenhuiQu WeiJiang MinYao GangDe Gruyterarticleimproved levenberg-marquardt algorithmneural networksatmospheric modelAstronomyQB1-991ENOpen Astronomy, Vol 30, Iss 1, Pp 24-35 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	improved levenberg-marquardt algorithm neural networks atmospheric model Astronomy QB1-991
spellingShingle	improved levenberg-marquardt algorithm neural networks atmospheric model Astronomy QB1-991 Cui Wenhui Qu Wei Jiang Min Yao Gang The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm
description	Traditional atmospheric models are based on the analysis and fitting of various factors influencing the space atmosphere density. Neural network models do not specifically analyze the polynomials of each influencing factor in the atmospheric model, but use large data sets for network construction. Two traditional atmospheric model algorithms are analyzed, the main factors affecting the atmospheric model are identified, and an atmospheric model based on neural networks containing various influencing factors is proposed. According to the simulation error, the Levenberg-Marquardt algorithm is used to iteratively realize the rapid network weight correction, and the optimal neural network atmospheric model is obtained. The space atmosphere is simulated and calculated with an atmospheric model based on neural networks, and its average error rate is lower than that of traditional atmospheric models such as the DTM2013 model and the MSIS00 model. At the same time, the calculation complexity of the atmospheric model based on the neural networks is significantly simplified than that of the traditional atmospheric model.
format	article
author	Cui Wenhui Qu Wei Jiang Min Yao Gang
author_facet	Cui Wenhui Qu Wei Jiang Min Yao Gang
author_sort	Cui Wenhui
title	The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm
title_short	The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm
title_full	The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm
title_fullStr	The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm
title_full_unstemmed	The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm
title_sort	atmospheric model of neural networks based on the improved levenberg-marquardt algorithm
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/73725b7e13e84cff8a06a8201f0566ca
work_keys_str_mv	AT cuiwenhui theatmosphericmodelofneuralnetworksbasedontheimprovedlevenbergmarquardtalgorithm AT quwei theatmosphericmodelofneuralnetworksbasedontheimprovedlevenbergmarquardtalgorithm AT jiangmin theatmosphericmodelofneuralnetworksbasedontheimprovedlevenbergmarquardtalgorithm AT yaogang theatmosphericmodelofneuralnetworksbasedontheimprovedlevenbergmarquardtalgorithm AT cuiwenhui atmosphericmodelofneuralnetworksbasedontheimprovedlevenbergmarquardtalgorithm AT quwei atmosphericmodelofneuralnetworksbasedontheimprovedlevenbergmarquardtalgorithm AT jiangmin atmosphericmodelofneuralnetworksbasedontheimprovedlevenbergmarquardtalgorithm AT yaogang atmosphericmodelofneuralnetworksbasedontheimprovedlevenbergmarquardtalgorithm
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The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm

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