TY - JOUR A1 - Pedreño Molina, Juan Luis AU - Monzó Cabrera, Juan AU - Sánchez Hernández, David Agapito T1 - A new predictive neural architecture for solving temperature inverse problems in microwave-assisted drying processes Y1 - 2004 UR - http://hdl.handle.net/10317/1476 AB - 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 KW - Teoría de la Señal y las Comunicaciones KW - Learning-based predictive system KW - Electric field estimation KW - Neural network modeling KW - Microwave-assisted drying applications KW - Inverse problem LA - eng PB - Elsevier ER -