@InProceedings{GomesSaFuLuChCa:2013:DaAsNe,
               author = "Gomes, Vitor Conrado Faria and Sambatti, Sabrina B and Furtado, 
                         Helaine C M and Luz, Eduardo F{\'a}vero Pacheco da and 
                         Char{\~a}o, Andrea Schwertner and Campos Velho, Haroldo Fraga 
                         de",
          affiliation = "{} and {} and {} and {} and Federal University of Santa Maria 
                         (UFSM), Santa Maria (RS), Brazil and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Data assimilation by neural network on hardware device",
            booktitle = "Proceedings...",
                 year = "2013",
                pages = "01--08",
         organization = "International Symposium on Inverse Problems, Design and 
                         Optimization, 4. (IPDO).",
            publisher = "Ecole des Mines d Albi",
                 note = "{Informa{\c{c}}{\~o}es Adicionais: Abstract A6382HC.}",
             keywords = "Data assimilation, Artificial neural network, FPGA (Field 
                         Programmable Gate Array), Wave equation.",
             abstract = "Data assimilation combines observation and data from a 
                         mathematical model to compute the best initial condition. Inverse 
                         problems can be classified according to the nature of the 
                         estimated property type: source term, system property, boundary 
                         condition, and initial condition. Extended Kalman filter (EKF) and 
                         four dimensional variational method (4D-Var) are two procedures 
                         employed to perform DA. Artificial neural networks are applied 
                         here for reducing the computational complexity for a given DA 
                         scheme. The supervised multi-layer perceptron is used to emulate 
                         the Kalman filter. The designed neural network was implemented on 
                         a FPGA (Field-programmable Gate Array), employed as a 
                         co-processor. The linear 1D wave equation is the dynamical system 
                         used for testing the framework. A good performance was obtained 
                         with neural network emulating the Kalman filter, with an average 
                         error 5.18E- 05 (software and FPGA comparison) on the FPGA 
                         implementation.",
  conference-location = "Albi",
      conference-year = "2013",
                label = "lattes: 5142426481528206 6 GomesSaFuLuChCa:2013:DaAsNe",
             language = "en",
           targetfile = "06382-fichier.pdf",
                  url = "http://ipdo2013.congres-scientifique.com/index.php?langue=en\&onglet=1\&acces=\&idUser=\&emailUser=",
        urlaccessdate = "21 maio 2024"
}