@InProceedings{AnochiCampSilv:2014:NeNeSt,
               author = "Anochi, Juliana Aparecida and Campos Velho, Haroldo Fraga de and 
                         Silva, Jos{\'e} Demisio Sim{\~o}es da",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Neural networks in the study of climate patterns seasonal",
            booktitle = "Proceedings...",
                 year = "2014",
         organization = "CCIS.",
             keywords = "Climate Prediction, Neural Networks, Rough Sets Theory.",
             abstract = "This work describes an Artificial Intelligence based technique to 
                         prepare data for constructing a climate prediction empirical model 
                         from reanalysis data in the South region of Brazil using 
                         Artificial Neural Network (ANN). The method uses Rough Sets Theory 
                         (RST) to reduce the amount of variables. The input of ANN there is 
                         two kinds of data: the variables chosen by the RST and full 
                         variables data to learn the seasonal behavior of the variable 
                         precipitation.",
  conference-location = "Asuncion",
      conference-year = "2014",
                label = "lattes: 2720072834057575 1 AnochiCampSilv:2014:NeNeSt",
             language = "en",
           targetfile = "Anochi_neural.pdf",
                  url = "http://ccis2014.pol.una.py/",
        urlaccessdate = "25 abr. 2024"
}