@Article{HärterCampRempChia:2008:NeNeAu,
author = "H{\"a}rter, Fabr{\'{\i}}cio Pereira and Campos Velho, Haroldo
Fraga de and Rempel, {\'E}rico Luiz and Chian, Abraham Chian
Long",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Tecnol{\'o}gico de Aeron{\'a}utica} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Neural networks in auroral data assimilation",
journal = "Journal of Atmospheric and Solar-Terrestrial Physics",
year = "2008",
volume = "70",
number = "10",
pages = "1243--1250",
month = "July",
keywords = "Auroral radio emissions, Nonlinear dynamics, Chaos, Data
assimilation, Kalman filter, Neural networks.",
abstract = "Data assimilation is an essential step for improving space weather
forecasting by means of a weighted combination between
observational data and data from a mathematical model. In the
present work data assimilation methods based on Kalman filter (KF)
and artificial neural networks are applied to a three-wave model
of auroral radio emissions. A novel data assimilation method is
presented, whereby a multilayer perceptron neural network is
trained to emulate a KF for data assimilation by using
cross-validation. The results obtained render support for the use
of neural networks as an assimilation technique for space weather
prediction.",
doi = "10.1016/j.jastp.2008.03.018",
url = "http://dx.doi.org/10.1016/j.jastp.2008.03.018",
issn = "1364-6826",
language = "en",
targetfile = "neural.pdf",
urlaccessdate = "29 jun. 2024"
}