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A new predictive neural architecture for solving temperature inverse problems in microwave-assisted drying processes
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, TcĂ°tĂž; 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 TcĂ°tĂž curve that best fits a desired temperature target in a
specific time slo
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