%0 Journal Article
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JHC3
%@resumeid 8JMKD3MGP5W/3C9JGUT
%@archivingpolicy denypublisher denyfinaldraft12
%3 1742-6596_285_1_012036.pdf
%X Description of a physical phenomenon through differential equations has errors involved, since the mathematical model is always an approximation of reality. For an operational prediction system, one strategy to improve the prediction is to add some information from the real dynamics into mathematical model. This aditional information consists of observations on the phenomenon. However, the observational data insertion should be done carefully, for avoiding a worse performance of the prediction. Technical data assimilation are tools to combine data from physical-mathematics model with observational data to obtain a better forecast. The goal of this work is to present the performance of the Neural Network Multilayer Perceptrons trained to emulate a Variational method in context of data assimilation. Techniques for data assimilation are applied for the Lorenz systems; which presents a strong nonlinearity and chaotic nature.
%N 012036
%T Neural networks for emulation variational method for data assimilation in nonlinear dynamics
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress elbert@lac.inpe.br
%@secondarytype PRE PI
%K Assimilação de Dados, Dinamica Nao-Linear, Controle Estocastico.
%@usergroup administrator
%@usergroup lattes
%@usergroup marciana
%@group
%@group LAC-CTE-INPE-MCT-BR
%@group LAC-CTE-INPE-MCT-BR
%@e-mailaddress elbert@lac.inpe.br
%@secondarykey INPE--PRE/
%@secondarymark C_ASTRONOMIA_/_FÍSICA B4_CIÊNCIA_DA_COMPUTAÇÃO C_CIÊNCIAS_BIOLÓGICAS_II C_ENGENHARIAS_I B3_ENGENHARIAS_II C_ENGENHARIAS_III B1_INTERDISCIPLINAR B3_MATERIAIS C_QUÍMICA
%F lattes: 0793627832164040 3 FurtadoCampMaca:2011:NeNeEm
%@issn 1742-6588
%2 dpi.inpe.br/plutao/2011/06.11.03.31.34
%@affiliation
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%B Journal of Physics: Conference Series
%4 dpi.inpe.br/plutao/2011/06.11.03.31.33
%@documentstage not transferred
%D 2011
%V 285
%@doi 10.1088/1742-6596/285/1/012036
%O Setores de Atividade: Atividades profissionais, científicas e técnicas.
%A Furtado, Helaine Cristina Morais,
%A de Campos Velho, Haroldo Fraga,
%A Macau, Elbert Einstein Nehrer,
%@dissemination WEBSCI; PORTALCAPES; COMPENDEX.
%@area COMP