Optimización en la elaboración de redes neuronales artifciales adaptativas usando una metodología de algoritmo de poda

Contenido principal del artículo

Luis Octavio González-Salcedo
Jorge Gotay-Sardiñas
Matías Roodschild
Adrian Luis Ernesto-Will
Sebastían Rodríguez

Resumen

Las redes neuronales artifciales feedforward y multicapa (RNA-MFF) han demostrado ser de gran alcance en la aproximación de funciones; sin embargo, su aplicación en problemas reales a menudo se limita a la experimentación del usuario, ya que la elección de arquitectura adecuada es un proceso que requiere conocimiento y experiencia. En este artículo se demuestra la capacidad de adaptación de la metodología del algoritmo de poda para encontrar el número óptimo de neuronas en la capa oculta de una RNA-MFF. La metodología se probó en dos conjuntos diferentes de problemas de referencia: modelación de la resistencia y del asentamiento, en hormigón de alto desempeño. Ambos conjuntos se utilizaron para analizar el tamaño de la arquitectura inicial de la red neuronal artifcial y para asegurar que el número superior propuesto de neuronas ocultas se pueda evitar en exceso.AbstractFeedforward and multi-layer artifcial neural networks (RNA-MFF) have been shown to be powerful in the approximation of functions; however, its application to real problems is often limited to user experimentation, since choosing the right architecture is a process that requires knowledge and experience. This article demonstrates the adaptability of the pruning algorithm methodology in fnding the optimal number of neurons in the hidden layer of an RNA-MFF. The methodology was tested on two different sets of reference problems: resistance and settling modeling, in high performance concrete. Both sets were used to analyze the initial architecture size of the artifcial neural network and to ensure that the proposed higher number of hidden neurons can be avoided in excess.Resumo As redes neurais artifciais avançadas e multicamadas feedforward (RNA-MFF) têm demostrado ter grande alcance na aproximação de funções; porém, a aplicação em problemas reais geralmente é limitada à experimentação do usuário, dado que escolher a arquitetura adequada é um processo que precisa conhecimento e experiência. Neste artigo foi demonstrada a adaptabilidade da metodologia do algoritmo de poda para encontrar o número ótimo de neurônios na camada oculta de uma RNA-MFF. A metodologia foi testada em dois conjuntos diferentes de problemas de referência: modelagem de resistência e sedimentação, em concreto de alto desempenho. Ambos os conjuntos foram utilizados para analisar o tamanho da arquitetura inicial da rede neural artifcial e garantir que o maior número de neurônios ocultos propostos possa ser evitado em excesso.

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Artículos Vol. 8-1

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