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El uso de perceptrones multicapa para la modelización estadística de series de tiempo no lineales de so2, en Salta Capital, Argentina
(Centro de Investigaciones en Matemática Pura y Aplicada (CIMPA) y Escuela de Matemática, San José, Costa Rica., 2013)
Una aproximación práctica a las redes neuronales artificiales.
(2017-08-28)
CONTENIDO: Generalidades sobre redes neuronales artificiales -- Redes neuronales perceptron y adaline -- Perceptron multicapa y algoritmo backpropagation -- Red neuronal de Hopfield -- Mapas auto-organizados de Kohonen -- ...
Perceptron
(2011-07)
We describe the Perceptron model, history, characteristics, training algorithm for solving classification problems, worked examples are also presented, as well as the limitations of this type of network.
Predição de dados estruturados utilizando a formulação Perceptron com aplicação em planejamento de caminhos
(Universidade Federal de Juiz de Fora (UFJF)BrasilICE – Instituto de Ciências ExatasPrograma de Pós-graduação em Modelagem ComputacionalUFJF, 2017)
Array of Multilayer Perceptrons with No-class Resampling Training for Face Recognition
(AEPIA, 2009-12)
A face recognition (FR) problem involves the face detection, representation and classification steps. Once a face is located in an image, it has to be represented through a feature extraction process, for later performing ...
Diseño de mina a cielo abierto utilizando redes neuronales de perceptrón multicapa
(Universidad de Talca (Chile). Facultad de Ingeniería, 2022)
Treinamento multiobjetivo de perceptron de múltiplas camadas comrepresentação esférica de pesos
(Universidade Federal de Minas GeraisUFMG, 2017-02-17)
This work presents a novel representation of artificial neural networks(ANN) multiobjective learning in which the weights are described basedon the Euclidean norm, which is taken as a measure of model complexity. Weights ...
Rainfall prediction methodology with binary multilayer perceptron neural networks
(2019-02-15)
Precipitation, in short periods of time, is a phenomenon associated with high levels of uncertainty and variability. Given its nature, traditional forecasting techniques are expensive and computationally demanding. This ...