@InCollection{NowosadRiosCamp:2002:DaAsCh,
author = "Nowosad, Alexandre Guirland and Rios Neto, Atais and Campos Velho,
Haroldo Fraga de",
title = "Data assimilation in chaotic dynamics using neural networks",
booktitle = "Nonlinear dynamics, chaos, control and their applications to
engineering sciences",
publisher = "ABCM/ABCM",
year = "2002",
editor = "Balthazar, Jos{\'e} Manoel and Gon{\c{c}}alves, Paulo Batista
and Brasil, Reyolando M. F. L. R. F and Caldas, Iber{\^e} L and
Rizatto, Felipe B. .",
pages = "cap. 1, 17--20",
keywords = "ENGENHARIA E TECNOLOGIA ESPACIAL, data assimilation, neural
networks, chaotics dynamics.",
abstract = "Multilayer Perceptron Neural Networks are used for data
assimilation in two nonlinear dynamic systems: the H{\'e}non and
Lorenz systems in chaotic state. The approach {"}emulates{"} the
Kalman Filter data assimilation method avoiding recalculation of
the gain matrix at each instant of assimilation. In the case of
H{\'e}non system an Adaptive Extended Kalman Filter was used to
provide examples for network training. In the case of Lorenz
System the Extended Kalman Filter was used for network training.
Preliminary test results obtained are shown.",
copyholder = "SID/SCD",
label = "10425",
seriestitle = "Nonlinear dynamics, chaos, control and their applications to
engineering sciences",
targetfile = "9494.pdf",
urlaccessdate = "29 jun. 2024"
}