Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653586 | Neurocomputing | 2005 | 8 Pages |
Abstract
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, Tc(t), 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 Tc(t) curve that best fits a desired temperature target in a specific time slot.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
J.L. Pedreño-Molina, J. Monzó-Cabrera, D. Sánchez-Hernández,