M
Marley M. B. R. Vellasco
Researcher at Pontifical Catholic University of Rio de Janeiro
Publications - 306
Citations - 2952
Marley M. B. R. Vellasco is an academic researcher from Pontifical Catholic University of Rio de Janeiro. The author has contributed to research in topics: Artificial neural network & Neuro-fuzzy. The author has an hindex of 24, co-authored 290 publications receiving 2519 citations. Previous affiliations of Marley M. B. R. Vellasco include University College London & The Catholic University of America.
Papers
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Proceedings ArticleDOI
Well Placement Optimization Using a Genetic Algorithm With Nonlinear Constraints
Alexandre A. Emerick,Eugenio Silva,Bruno Messer,Luciana Faletti Almeida,Dilza Szwarcman,Marco Aurélio Cavalcanti Pacheco,Marley M. B. R. Vellasco +6 more
TL;DR: The developed software is the result of a two-year project focused on a robust implementation of a computer-aided optimization tool to deal with realistic well placement problems with arbitrary well trajectories, complex model grids and linear and nonlinear constraints.
Journal ArticleDOI
VLSI architectures for neural networks
TL;DR: An introduction to neural networks and neural information processing is provided, and neurocomputers are discussed, focusing on how their design exploits the architectural properties of VLSI circuits.
Journal ArticleDOI
Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases
L.B. Goncalves,Marley M. B. R. Vellasco,Marco Aurélio Cavalcanti Pacheco,Flávio Joaquim de Souza +3 more
TL;DR: The HNFB/sup -1/ model is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model, which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs.
Book ChapterDOI
Quantum-Inspired Evolutionary Algorithm for Numerical Optimization
TL;DR: A novel EA for numerical optimization inspired by the multiple universes principle of quantum computing is proposed and results show that this algorithm can find better solutions, with less evaluations, when compared with similar algorithms.
Proceedings Article
Data Mining Techniques on the Evaluation of Wireless Churn
Jorge Brantes Ferreira,Marley M. B. R. Vellasco,Marco Aurélio Cavalcanti Pacheco,Carlos Roberto Hall Barbosa +3 more
TL;DR: Analytical methods and models intrinsic to decision technology and machine learning are evaluated, in an effort to provide the necessary intelligence to identify and understand troublesome customers in order to act upon them before they churn.