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

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.

• Autores: Martínez Gómez, Edinson Jabid
• Tipo de recurso: Trabajo de grado de pregrado
• Fecha de publicación: 2013
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: spa
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/1253
• Acceso en línea: https://hdl.handle.net/11323/1253; https://repositorio.cuc.edu.co/
• Palabra clave: Computadores neuronales; Herramientas informáticas; Redes neuronales; Redes neuronales artificiales; Inteligencia artificial; Redes neuronales artificiales y series de tiempo; Neural computers; Computer tools; Neural networks; Artificial neural networks; Artificial intelligence; Artificial neural networks and time series
• Rights: openAccess
• License: Atribución – No comercial – Compartir igual