Title: A new predictive neural architecture for modelling electric field patterns in microwave-heating processes

Authors: J.L. Pedreno-Molina, J. Monzo-Cabrera, M. Pinzolas, M.E. Requena Perez

Addresses: Department of Information Technologies and Communications, Technical University of Cartagena, Campus Muralla del Mar s/n, E-30.202, Cartagena, Murcia, Spain. ' Department of Information Technologies and Communications, Technical University of Cartagena, Campus Muralla del Mar s/n, E-30.202, Cartagena, Murcia, Spain. ' Department of Systems Engineering and Automation, Technical University of Cartagena, Campus Muralla del Mar s/n, E-30.202, Cartagena, Murcia, Spain. ' Department of Information Technologies and Communications, Technical University of Cartagena, Campus Muralla del Mar s/n, E-30.202, Cartagena, Murcia, Spain

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. ε*=ε′−jε″) 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 (ε′, ε″) 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 training processes.

Keywords: electric field estimation; learning based predictive systems; microwave-assisted applications; microwave heating oven; neural networks; modelling; electric field patterns; simulation; finite element method; FEM.

DOI: 10.1504/IJMPT.2007.013122

International Journal of Materials and Product Technology, 2007 Vol.29 No.1/2/3/4, pp.185 - 199

Published online: 09 Apr 2007 *

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