Ponencia
Neurofuzzy model of an industrial process, reducing complexity by using principal component analysis
Autor/es | Escaño González, Juan Manuel
Bordons Alba, Carlos |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2012 |
Fecha de depósito | 2020-01-10 |
Publicado en |
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ISBN/ISSN | 9788461566532 |
Resumen | A Neurofuzzy model of a mixing chamber pressure has been proposed. The process is a part of
a copper smelter plant. The principal component
analysis (PCA) method has been used to reduce
the inputs space for a recurrent ... A Neurofuzzy model of a mixing chamber pressure has been proposed. The process is a part of a copper smelter plant. The principal component analysis (PCA) method has been used to reduce the inputs space for a recurrent fuzzy model. The coupling among variables and their mutual influence between themselves, are taken into account by the projection into the PCA axis. The model have been validated with real data from the factory. The validation result shows that the model is suitable for simulation. |
Identificador del proyecto | DPI2010-21589-C05-01 |
Cita | Escaño González, J.M. y Bordons Alba, C. (2012). Neurofuzzy model of an industrial process, reducing complexity by using principal component analysis. En Congreso Español sobre Tecnologías y Lógica Fuzzy (108-113), Valladolid: Universidad de Valladolid. |
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