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Aplicación de una red neuronal feed-forward backpropagation para el diagnóstico de fallas mecánicas en motores de encendido provocadoApplication of feed-forward backpropagation neural network for the diagnosis of mechanical failures in engines provoked ignition
(Universidad Politécnica Salesiana, 2018)
Aplicación de una red neuronal feed-forward backpropagation para el diagnóstico de fallas mecánicas en motores de encendido provocado
(2019-01)
En la presente investigación se explica la metodología para la creación de un sistema de diagnóstico aplicado a la detección de fallas mecánicas en vehículos con motores a gasolina mediante redes neuronales
artificiales, ...
Neural Network Model for Land Cover Classification from Satellite Images
(Instituto de Investigaciones Agropecuarias, INIA, 2007)
ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory
(Editorial Board, 2018)
This paper presents a variation in the algorithm EMODS (Evolutionary Metaheuristic of Deterministic Swapping), at the level of its mutation stage in order to train algorithms for
each problem. It should be noted that the ...
Determination of organic matter in soils using radial basis function networks and near infrared spectroscopy
(Elsevier Science BvAmsterdamHolanda, 2002)
TEXTNN-A MATLAB program for textural classification using neural networks
(Pergamon-elsevier Science LtdOxfordInglaterra, 2009)
Prediction of modulus of elasticity and compressive strength of concrete specimens by means of artificial neural networks
(Univ Estadual Maringa, Pro-reitoria Pesquisa Pos-graduacao, 2016-01-01)
Currently, artificial neural networks are being widely used in various fields of science and engineering. Neural networks have the ability to learn through experience and existing examples, and then generate solutions and ...
Aplicación de La Red Neuronal Artificial Feedforward Backpropagation para la predicción de demanda de energía eléctrica en la Empresa Eléctrica Riobamba S.A.
(Escuela Superior Politécnica de Chimborazo, 2017-10)
This research proposes a model based on the artificial neural network Feedforward Back
propagation capable of predicting the demand of electric power with a percentage of absolute
error lower than the one generated due ...