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Revista Colombiana de Estadística
Print version ISSN 0120-1751
Rev.Colomb.Estad. vol.41 no.2 Bogotá July/Dec. 2018
https://doi.org/10.15446/rce.v41n2.55250
Artículos originales de investigación
Artificial Neuronal Networks: A Bayesian Approach Using Parallel Computing
Redes neuronales regularizadas: un enfoque bayesiano usando cómputo paralelo
1 Estadística, Socio Economía Estadística e Informática, Colegio de Postgraduados, Texcoco, México.
2 Matemáticas y Cómputo, Departamento de Irrigación, Universidad Autónoma Chapingo, Texcoco, México.
An Artificial Neural Network (ANN) is a learning paradigm and automatic processing inspired in the biological behavior of neurons and the brain structure. The brain is a complex system; its basic processing unit are the neurons, which are distributed massively in the brain sharing multiple connections between them. The ANNs try to emulate some characteristics of humans, and can be thought as intelligent systems that perform some tasks in a dierent way that actual computer does. The ANNs can be used to perform complex activities, for example: pattern recognition and classification, weather prediction, genetic values prediction, etc. The algorithms used to train the ANN, are in general complex, so therefore there is a need to have alternatives which lead to a significant reduction of times employed to train an ANN. In this work, we present an algorithm based in the strategy “divide and conquer” which allows to train an ANN with a single hidden layer. Part of the sub problems of the general algorithm used for training are solved by using parallel computing techniques, which allows to improve the performance of the resulting application. The proposed algorithm was implemented using the C++ programming language, and the libraries Open MPI and ScaLAPACK. We present some application examples and we asses the application performance. The results shown that it is possible to reduce significantly the time necessary to execute the program that implements the algorithm to train the ANN.
Key words: Empirical Bayes; Nonlinear models; Parallel processing
Una Red Neuronal Artificial (RNA) es un paradigma de aprendizaje y procesamiento automático inspirado en el comportamiento biológico de las neuronas y en la estructura del cerebro. El cerebro es un sistema altamente complejo; su unidad básica de procesamiento son las neuronas, las cuales se encuentra distribuidas de forma masiva compartiendo múltiples conexiones entre ellas. Las RNAs intentan emular ciertas características propias de los humanos, pueden ser vistas como un sistema inteligente que lleva a cabo tareas de manera distinta a como lo hacen las computadoras actuales. Las RNAs pueden emplearse para realizar actividades complejas, por ejemplo: reconocimiento y clasificación de patrones, predicción del clima, predicción de valores genéticos, etc. Los algoritmos utilizados para entrenar las redes, son en general complejos, por lo cual surge la necesidad de contar con alternativas que permitan reducir de manera significativa el tiempo necesario para entrenar una red. En este trabajo se presenta una propuesta de algoritmos basados en la estrategia “divide y conquista” que permiten entrenar las RNAs de una sola capa oculta. Parte de los sub problemas del algoritmo general de entrenamiento se resuelven utilizando técnicas de cómputo paralelo, lo que permite mejorar el desempeño de la aplicación resultante. El algoritmo propuesto fue implementado utilizando el lenguaje de programación C++, así como las librerías Open MPI y ScaLAPACK. Se presentan algunos ejemplos de aplicación y se evalúa el desempeño del programa resultante. Los resultados obtenidos muestran que es posible reducir de manera significativa los tiempos necesarios para ejecutar el programa que implementa el algoritmo para el ajuste de la RNA.
Palabras clave: Bayes empírico; Modelos no lineales; Procesamiento en paralelo
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