@Article{AnochiTorrCamp:2020:TwGeAp,
               author = "Anochi, Juliana Aparecida and Torres, Reynier Hern{\'a}ndez and 
                         Campos Velho, Haroldo Fraga de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Two geoscience applications by optimal neural network 
                         architecture",
              journal = "Pure and Applied Geophysics",
                 year = "2020",
               volume = "177",
               number = "6",
                pages = "2663--268",
                month = "June",
             keywords = "Metaheuristics, optimization problem, neural network, data 
                         assimilation, climate precipitation prediction, monoobjective 
                         problem, multi-objective problem.",
             abstract = "Nowadays, artificial neural networks have been successfully 
                         applied on several research and application fields. An appropriate 
                         configuration for a neural network is a complex task, and it often 
                         requires the knowledge of an expert on the application. A 
                         technique for automatic configuration for a neural network is 
                         formulated as an optimization problem. Two strategies are 
                         considered: a mono-objective minimization problem, using 
                         multiparticle collision algorithm (MPCA); and a multi-objective 
                         minimization problem addressed by the non-dominated sorting 
                         genetic algorithm (NSGA-II). The proposed optimization approaches 
                         were tested for two application in geosciences: data assimilation 
                         for wave evolution equation, and the mesoscale seasonal climate 
                         prediction for precipitation. Better results with automatic 
                         configuration were obtained for data assimilation than those 
                         obtained by network defined by an expert. For climate seasonal 
                         precipitation, automatic configuration presented better 
                         predictions were presented than ones carried out by an expert. For 
                         the worked examples, the NSGA-II presented a superior result for 
                         the worked experiments.",
                  doi = "10.1007/s00024-019-02386-y",
                  url = "http://dx.doi.org/10.1007/s00024-019-02386-y",
                 issn = "0033-4553",
             language = "en",
           targetfile = "anochi_two.pdf",
        urlaccessdate = "24 abr. 2024"
}