@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 = "11 maio 2024"
}