Neural networks with radial basis applied to the improve of quality

Descripción del Articulo

This research has led to construct an artificial neural network ARN with Radial Basis Function, and using Mahalanobis distance RND, for improving the quality of process design, which have performed better than those obtained with the for traditional statistical analysis of design of experiments and...

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Detalles Bibliográficos
Autor: Cevallos Ampuero, Juan
Formato: artículo
Fecha de Publicación:2008
Institución:Universidad Nacional Mayor de San Marcos
Repositorio:Revistas - Universidad Nacional Mayor de San Marcos
Lenguaje:español
OAI Identifier:oai:ojs.csi.unmsm:article/6052
Enlace del recurso:https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/6052
Nivel de acceso:acceso abierto
Materia:Neural networks with radial basis
Radial basis functions
Neural networks of exact design
Multilayer perceptron with backpropagation learning.
Redes neuronales de base radial
Funciones de base radial
Redes neuronales de diseño exacto
Perceptrón multicapa con aprendizaje backpropagation
Descripción
Sumario:This research has led to construct an artificial neural network ARN with Radial Basis Function, and using Mahalanobis distance RND, for improving the quality of process design, which have performed better than those obtained with the for traditional statistical analysis of design of experiments and other RNA that already exist, for cases that are working with several independent and dependent variables in which its relations are not linear. It also allows with the RND obtain input parameters to achieve a desired level of quality, for it applies a methodology that uses RNAReverse and Direct at once.
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