@InProceedings{ShiguemoriCampSilv:2008:NeNoAr,
               author = "Shiguemori, Elcio Hideiti and Campos Velho, Haroldo Fraga de and 
                         Silva, Jos{\'e} Dem{\'{\i}}sio Sim{\~o}es da",
          affiliation = "{General Command for Aerospace Technology} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Atmospheric Temperature Retrieval from Satellite Data: New 
                         Non-extensive Artificial Neural Network Approach",
            booktitle = "Proceedings...",
                 year = "2008",
         organization = "ACM Symposium on Applied Computing, 27. (SIGAPP).",
            publisher = "ACM",
              address = "New York",
             abstract = "In this paper, vertical temperature profiles are inferred by 
                         neural networks based inverse procedure from satellite data, 
                         non-linear function estimation. A new approach to classical Radial 
                         Basis Function neural network is trained using data provided by 
                         the direct model characterized by the Radiative Transfer Equation 
                         (RTE). The neural network results are compared to the ones 
                         obtained from classical neural networks Radial Basis Function and 
                         traditional method to solve inverse problems, the regularization. 
                         In addition, real radiation data from the HIRS/2 - High Resolution 
                         Infrared Radiation Sounder - is used as input for the neural 
                         networks to generate temperature profiles that are compared to 
                         measured temperature profiles from radiosonde. Analysis of the new 
                         approach results reveals the generated profiles closely 
                         approximate the results obtained with classical neural networks 
                         and regularized inversions, [5] [15], thus showing adequacy of 
                         neural network based models in solving the inverse problem of 
                         temperature retrieval from satellite data. The advantages of using 
                         neural network based systems are related to their intrinsic 
                         features of parallelism; after trained, the networks are much 
                         faster than regularized approaches, and hardware implementation 
                         possibilities that may imply in very fast processing systems.",
  conference-location = "New York",
      conference-year = "16-20 Mar.",
                  doi = "10.1145/1363686.1364087",
                  url = "http://dx.doi.org/10.1145/1363686.1364087",
                 isbn = "978-1-59593-753-7",
             language = "en",
           targetfile = "p1688-hideiti.pdf",
        urlaccessdate = "07 maio 2024"
}