Neural networks with radial basis applied to the improve of quality

• 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

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.