%0 Journal Article 
%A Pedreño Molina, Juan Luis
%A Monzó Cabrera, Juan
%A Sánchez Hernández, David Agapito
%T A new predictive neural architecture for solving temperature inverse problems in microwave-assisted drying processes
%D 2004
%U http://hdl.handle.net/10317/1476
%X In this paper, a novel learning architecture based on neural networks is used for temperature
inverse modeling in microwave-assisted drying processes. The proposed design combines the
accuracy of the radial basis functions (RBF) and the algebraic capabilities of the matrix
polynomial structures by using a two-level structure. This architecture is trained by
temperature curves, TcðtÞ; previously generated by a validated drying model. The
interconnection of the learning-based networks has enabled the finding of electric field (E)
optimal values which provide the TcðtÞ curve that best fits a desired temperature target in a
specific time slot
%K Teoría de la Señal y las Comunicaciones
%K Learning-based predictive system
%K Electric field estimation
%K Neural network modeling
%K Microwave-assisted drying applications
%K Inverse problem
%~ GOEDOC, SUB GOETTINGEN