@Article{HärterCamp:2008:NeApAp,
               author = "H{\"a}rter, Fabr{\'{\i}}cio Pereira and Campos Velho, Haroldo 
                         Fraga de",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "New approach to applying neural network in nonlinear dynamic 
                         model",
              journal = "Applied Mathematical Modelling",
                 year = "2008",
               volume = "32",
               number = "12",
                pages = "2621--2633",
             keywords = "dynamo model, data assimilation, extended Kalman filter, 
                         artificial neural network, radial base function.",
             abstract = "In this work, radial basis function neural network (RBF-NN) is 
                         applied to emulate an extended Kalman filter (EKF) in a data 
                         assimilation scenario. The dynamical model studied here is based 
                         on the one-dimensional shallow water equation DYNAMO-1D. This code 
                         is simple when compared with an operational primitive equation 
                         models for numerical weather prediction. Although simple, the 
                         DYNAMO-1D is rich for representing some atmospheric motions, such 
                         as Rossby and gravity waves. It has been shown in the literature 
                         that the ability of the EKF to track nonlinear models depends on 
                         the frequency and accuracy of the observations and model errors. 
                         In some cases, just fourth-order moment EKF works well, but will 
                         be unwieldy when applied to high-dimensional state space. 
                         Artificial Neural Network (ANN) is an alternative solution for 
                         this computational complexity problem, once the ANN is trained 
                         offline with a high order Kalman filter, even though this Kalman 
                         filter has high computational cost (which is not a problem during 
                         ANN training phase). The results achieved in this work encourage 
                         us to apply this technique on operational model. However, it is 
                         not yet possible to assure convergence in high dimensional 
                         problems.",
                  doi = "10.1016/j.apm.2007.09.006",
                  url = "http://dx.doi.org/10.1016/j.apm.2007.09.006",
                 issn = "0307-904X",
                label = "lattes: 5142426481528206 2 H{\"a}rterCamp:2008:NeApAp",
             language = "en",
           targetfile = "1-s2.0-S0307904X07002296-main.pdf",
        urlaccessdate = "21 maio 2024"
}