@InProceedings{CortivoChalCamp:2012:CoMLAd,
               author = "Cortivo, Fabio Dall and Chalhoub, Ezzat Selim 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 = "A committee of MLP with adaptive slope parameter trained by the 
                         quasi-Newton method to solve problems in hydrologic optics",
            booktitle = "Proceedings...",
                 year = "2012",
                pages = "1--8",
         organization = "International Joint Conference on Neural Networks, (IJCNN).",
            publisher = "Institute of Electrical and Electronics Engineers",
              address = "Piscataway",
                 note = "{Setores de Atividade: Educa{\c{c}}{\~a}o.}",
             keywords = "Hydrologic optics, Multi layer perceptron, Phase functions, 
                         Quasi-Newton methods, Single scattering albedo, Artificial Neural 
                         Networks, Multilayer Perceptron, Backpropagation, Quasi-Newton 
                         Method, hydrologic optics, Single Scattering Albedo.",
             abstract = "Artificial Neural Networks (ANNs) can be used to solve problems in 
                         Hydrologic Optics. A relevant problem is the estimation of the 
                         single scattering albedo and the phase function parameters, from 
                         the emitted radiation at the surface of natural waters. In this 
                         work we use a committee of ANNs of Multilayer Perceptron type to 
                         perform the estimation of the two mentioned parameters. The 
                         training of each network is formulated as a nonlinear optimization 
                         problem subject to constraints. In addition, each activation 
                         function has a distinct slope parameter, that is initially chosen 
                         by a random number generator function. This set of parameter 
                         (slopes) was included within the free variables network set in 
                         order to be adjusted to reach optimal values, together with the 
                         weights and biases, during the network training. This procedure 
                         (slope parameters inclusion) makes each one of the activation 
                         functions to have a different slope. Each network that composes 
                         the committee was trained independently, in order to become expert 
                         for the estimation of only one of the hydrologic parameters. For 
                         the networks training, we used the quasi-Newton method that is 
                         implemented in E04UCF subroutine, in the NAG library, developed by 
                         the Numerical Algorithms Group - NAG. The use of the quasi-Newton 
                         method to train the networks together with the distinct slope 
                         parameters resulted in a network with a fast learning and 
                         excellent generalization. Once the networks were trained, they 
                         were grouped so to share the input patterns, but remained 
                         independent from one another. For the validation/generalization 
                         test we used two distinct sets. For all considered noise levels, 
                         we obtained 100% of correct answers for the first set, and above 
                         90% of correct answers for the second se.",
  conference-location = "Brisbane",
      conference-year = "10-15 June 2012",
                  doi = "10.1109/IJCNN.2012.6252665",
                  url = "http://dx.doi.org/10.1109/IJCNN.2012.6252665",
                 isbn = "978-146731490-9",
                 issn = "1098-7576",
                label = "lattes: 8068157900374950 2 CortivoChalVelh:2012:CoMLAd",
             language = "en",
         organisation = "IEEE Computational Intelligence Society (CIS); International 
                         Neural Network Society (INNS",
           targetfile = "cortivo_committee.pdf",
        urlaccessdate = "30 abr. 2024"
}