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Neural Computing and Applications

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21-10-2019 | Original Article

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

Authors: Dinibel Pérez Bello, María P. Natali, Amalia Meza

Published in: Neural Computing and Applications | Issue 12/2019

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Abstract

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.

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Title: Comparison of adaptive neuro-fuzzy inference system and recurrent neural network in vertical total electron content forecasting
Authors: Dinibel Pérez Bello
María P. Natali
Amalia Meza
Publication date: 21-10-2019
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 12/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04528-8

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