@InProceedings{AnochiCampHern:2016:ClPrPr,
author = "Anochi, Juliana and Campos Velho, Haroldo Fraga de and
Hern{\'a}ndez Torres, Reynier",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Climate precipitation prediction using optimal neural network
architecture in Southeast Region of Brazil",
booktitle = "Proceedings...",
year = "2016",
organization = "Conference on Computational Interdisciplinary Sciences, 14.
(CCIS16)",
keywords = "metaheuristic, optimization problem, neural networks, climate
prediction, mono-objective problem, multiobjective problem.",
abstract = "Neural network is a technique successfully employed in many
applications on several research fields. Despite the potential of
a neural network model, its performance is dependent on the
definition of the parameters, since the definition of architecture
(topology) can significantly influence the training process. Here,
a technique for automatic configuration for a neural network is
described as an optimization problem combining two different
optimization schemes: a mono-objective minimization problem using
Multi-Particle Collision Algorithm (MPCA), and a multiobjective
minimization problem Nondominated Sorting Genetic Algorithm
(NSGA-II). The proposed optimization approaches were tested for
the mesoscale seasonal climate prediction for precipitation. The
meteorological data were processed by Rough Set Theory to extract
relevant information to perform the climate prediction by neural
network for the Southeast region of Brazil, with a reduced data
set.",
conference-location = "S{\~a}o Jos{\'e} dos Campos, SP",
conference-year = "7-10 nov",
language = "en",
targetfile = "anochi_climate.pdf",
urlaccessdate = "30 nov. 2020"
}