%0 Journal Article
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP 8JMKD3MGPCW/3ET76KE
%@archivingpolicy denypublisher denyfinaldraft
%@issn 1676-2789
%@dissemination PORTALCAPES
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JHH2
%X This work presents an Artificial Intelligence based approach for attribute reduction of reanalysis climate data to build a climate forecasting model using artificial neural networks. The methodology uses Rough Sets Theory for retrieving re-levant information from the available data, thus reducing the correlation redundancy among the variables used for forecasting purposes. Neural network based forecasting models are developed for Northeast Brazil, by learning the seasonal behavior of the precipitation variable.
%@mirrorrepository sid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
%N 2
%T Uso de redes neurais artificiais e teoria de conjuntos aproximativos no estudo de padrões climáticos sazonais
%@electronicmailaddress juliana.anochi@lac.inpe.br
%@electronicmailaddress demisio@lac.inpe.br
%@secondarytype PRE PN
%@usergroup administrator
%@usergroup banon
%@usergroup juliana.anochi@lac.inpe.br
%@group LAC-CTE-INPE-MCT-BR
%@group CTE-CTE-INPE-MCT-BR
%@e-mailaddress juliana.anochi@gmail.com
%3 vol7-no2-art5_Juliana.pdf
%U http://www.deti.ufc.br/~lnlm/
%2 sid.inpe.br/mtc-m19/2010/08.25.12.38.34
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%B Learning and Nonlinear Models
%4 sid.inpe.br/mtc-m19/2010/08.25.12.38
%D 2009
%V 7
%A Anochi, Juliana Aparecida,
%A Silva, José Demisio Simões da,
%@area COMP