期刊
NEUROCOMPUTING
卷 64, 期 -, 页码 521-528出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2004.11.026
关键词
learning-based predictive system; electric field estimation; neural network modeling; microwave-assisted drying applications; inverse problem
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, T-c(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 T-c(t) curve that best fits a desired temperature target in a specific time slot. (c) 2005 Elsevier B.V. All rights reserved.
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