Browsing by Subject "Redes neuronales artificiales"

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Demand forecasting method using artificial neural networks

amelec, viloria; Arrieta Matos, Fernanda; Gaitán, Mercedes; Hernández Palma, Hugo; Flórez Guzman, Yasmin; CABAS VASQUEZ, LUIS CARLOS; Vargas Mercado, Carlos; Pineda Lezama, Omar Bonerge (Universidad de la Costa, 2019)

Based on a forecast, the decision maker can determine the capacity required to meet a certain forecast demand, as well as carry out in advance the balance of capacities in order to avoid underusing or bottlenecks. This ...
Implementación web de redes neuronales artificiales aplicadas a la predicción de series de tiempo

Martínez Gómez, Edinson Jabid (Ingeniería de Sistemas, 2013)

In this Project we show the development and deployment of a web platform with two kinds of artificial Neural Networks applied to forecast time series. It has been developed with the language of programming Python and use ...

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Demand forecasting method using artificial neural networks
...
amelec, viloria | 2019

Based on a forecast, the decision maker can determine the capacity required to meet a certain forecast demand, as well as carry out in advance the balance of capacities in order to avoid underusing or bottlenecks. This article proposes a procedure for forecasting demand through Artificial Neural Networks. In order to carry out the validation, the procedure proposed was applied in a Soda Trading and Distribution Company where three types of products were selected

LEER
Implementación web de redes neuronales artificiales aplicadas a la predicción de series de tiempo
...
Martínez Gómez, Edinson Jabid | 2013

In this Project we show the development and deployment of a web platform with two kinds of artificial Neural Networks applied to forecast time series. It has been developed with the language of programming Python and use Extjs 4 for the client side. Allows the simulation of multilayer perceptron and neural networks based on radial basis functions. For the first algorithm it is used as the resilient backpropagation learning, which attempts to minimize the mean square error function to adjust the network weights. The training process of the RBF network is performed in two phases, using initially unsupervised learning, through the algorithm of k-means, for the centers of the radial basis function, subsequently standard deviations are found by the LMS algorithm and adjustment of the weights is obtained with the rule of the pseudo-inverse. The inputs to the simulation platform have to be imported through files (.csv) format, once we have obtained the results are graphically represented each of them. Simulations performed on a time series basis allow to obtain a good approximation in that is known if the variable value will increase or decrease. But because the algorithms implemented require expensive hardware, we need to look for alternatives such as parallel computing and optimization algorithms implemented.

LEER

Browsing by Subject "Redes neuronales artificiales"

Demand forecasting method using artificial neural networks ﻿

Implementación web de redes neuronales artificiales aplicadas a la predicción de series de tiempo ﻿

Envíos recientes

Demand forecasting method using artificial neural networks

Implementación web de redes neuronales artificiales aplicadas a la predicción de series de tiempo

Demand forecasting method using artificial neural networks

Implementación web de redes neuronales artificiales aplicadas a la predicción de series de tiempo