%0 Journal Article
%4 sid.inpe.br/mtc-m18@80/2008/06.26.19.09
%2 sid.inpe.br/mtc-m18@80/2008/06.26.19.09.04
%@doi 10.1016/j.jastp.2008.03.018
%@issn 1364-6826
%T Neural networks in auroral data assimilation
%D 2008
%8 July
%A Härter, Fabrício Pereira,
%A Campos Velho, Haroldo Fraga de,
%A Rempel, Érico Luiz,
%A Chian, Abraham Chian Long,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Tecnológico de Aeronáutica
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%B Journal of Atmospheric and Solar-Terrestrial Physics
%V 70
%N 10
%P 1243-1250
%K Auroral radio emissions, Nonlinear dynamics, Chaos, Data assimilation, Kalman filter, Neural networks.
%X 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.
%@language en
%3 neural.pdf