Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting

Accurate prediction of total electron content (TEC) is important for monitoring the behavior of the ionosphere and indeed a magnitude of interest to understand the properties and behavior of the Sun–Earth System. The conditions of this medium have a direct impact on a growing variety of critical tec...

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Detalles Bibliográficos
Autores principales: Pérez Bello, Dinibel, Natali, María Paula, Meza, Amalia Margarita
Formato: Articulo
Lenguaje:Inglés
Publicado: 2019
Materias:
Astronomía
vTEC
Space weather
Neural network
Forecasting
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/142868
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-142868
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Astronomía
vTEC
Space weather
Neural network
Forecasting
spellingShingle Astronomía
vTEC
Space weather
Neural network
Forecasting
Pérez Bello, Dinibel
Natali, María Paula
Meza, Amalia Margarita
Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting
topic_facet Astronomía
vTEC
Space weather
Neural network
Forecasting
description Accurate prediction of total electron content (TEC) is important for monitoring the behavior of the ionosphere and indeed a magnitude of interest to understand the properties and behavior of the Sun–Earth System. The conditions of this medium have a direct impact on a growing variety of critical technological infrastructure. This work presents a comparison between two different artificial neural networks (ANNs): an adaptive neuro-fuzzy inference system and nonlinear autoregressive neural network (NAR-NN) applied to TEC. Both ANNs where tested on four different geomagnetic locations on 4 1-week periods having a variety of geomagnetic disturbance levels. The effect of using different training period lengths and the system response for 60 and 30 min sampling rate TEC time series was investigated. NAR-NN shows a slightly better performance, being the higher difference during the greater perturbations. There is also a better response when sampling rates of 30 min are used.
format Articulo
Articulo
author Pérez Bello, Dinibel
Natali, María Paula
Meza, Amalia Margarita
author_facet Pérez Bello, Dinibel
Natali, María Paula
Meza, Amalia Margarita
author_sort Pérez Bello, Dinibel
title Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting
title_short Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting
title_full Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting
title_fullStr Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting
title_full_unstemmed Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting
title_sort comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting
publishDate 2019
url http://sedici.unlp.edu.ar/handle/10915/142868
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AT natalimariapaula comparisonofadaptiveneurofuzzyinferencesystemandrecurrentneuralnetworkinverticaltotalelectroncontentforecasting
AT mezaamaliamargarita comparisonofadaptiveneurofuzzyinferencesystemandrecurrentneuralnetworkinverticaltotalelectroncontentforecasting
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