@InProceedings{AnochiCamp:2014:OpFeNe,
               author = "Anochi, Juliana A. and Campos Velho, Haroldo Fraga",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Optimization of feedforward neural network by Multiple Particle 
                         Collision Algorithm",
            booktitle = "Proceedings...",
                 year = "2014",
                pages = "128--134",
         organization = "IEEE Symposium Series on Computational Intelligence.",
            publisher = "IEEE",
             abstract = "Optimization of neural network topology, weights and neuron 
                         activation functions for given data set and problem is not an easy 
                         task. In this article, a technique for automatic configuration of 
                         parameters topology for feedforward artificial neural networks 
                         (ANN) is presented. The determination of optimal parameters is 
                         formulated as an optimization problem, solved with the use of 
                         meta-heuristic Multiple Particle Collision Algorithm (MPCA). The 
                         self-configuring networks are applied to predict the mesoscale 
                         climate for the precipitation field. The results obtained from the 
                         neural network using the method of data reduction by the Theory of 
                         Rough Sets and the self-configuring network by MPCA were 
                         compared.",
  conference-location = "Orlando, FL",
      conference-year = "9-12 Dec.",
                  doi = "10.1109/FOCI.2014.7007817",
                  url = "http://dx.doi.org/10.1109/FOCI.2014.7007817",
             language = "en",
           targetfile = "anochi_optimization.pdf",
        urlaccessdate = "26 abr. 2024"
}