A New Predictive Neural Architecture for Modelling Electric Field Patterns Within Dielectric Materials in Industrial Microwave-Heating Processes
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URI: http://hdl.handle.net/10317/1475Share
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Pedreño Molina, Juan Luis; Monzó Cabrera, Juan; Pinzolas Prado, Miguel; Requena Pérez, María EugeniaResearch Group
Electromagnetismo y MateriaKnowledge Area
Teoría de la Señal y las ComunicacionesPublication date
2007Publisher
Inderscience InterprisesBibliographic Citation
PEDREÑO MOLINA, J.L., MONZÓ CABRERA, J., PINZOLAS PRADO, M., REQUENA PÉREZ, M.E. New Predictive Neural Architecture for Modelling Electric Field Patterns Within Dielectric Materials in Industrial Microwave-Heating Processes. International Journal of Materials and Product Technology, vol. 29 : 185-199, 2007. ISSN (online) 1741-5209Peer review
SíKeywords
Electric field estimationLearning-based predictive system
Microwave-assisted applications
Microwave-heating oven
Neural network modelling
Estimación de campo eléctrico
Sistema predictivo basado en el aprendizaje
Aplicaciones asistidas por microondas
Calentamiento por horno de microondas
Modelos de redes neuronales
Abstract
In this work, a learning architecture based on neural networks has
been employed for modelling the electric field pattern along an axis of a
multimode microwave-heating cavity that contains dielectric materials. The
multilevel configuration of this architecture, based on Radial Basis
Functions (RBF) and polynomial structures, allows the fitting of the
electric field as a function of the dielectric parameters (i.e. " "0 ÿ j"00)
along one axis (x) of the cavity as well as inside the sample. In the learning
stage, different samples have trained the neural architecture, by means of
the mapping between ("0; "00) and the absolute value of the electric field
pattern, generated with a 2D simulation platform based on the Finite
Elements Method (FEM). The results obtained with conventional samples,
such as polyester, epoxy, silicon crystal or beef steak, show that the
proposed neural model is able to accurately predict the electric field spatial
distribution under appropriate ...
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