Evaluation of multilayer perceptron neural networks and adaptive neuro-fuzzy inference systems for the mass transfer modeling of Echium amoenum Fisch. & C. A. Mey.
Journal:
Journal of the science of food and agriculture
PMID:
34000064
Abstract
BACKGROUND: Multilayer perceptron (MLP) feed-forward artificial neural networks (ANN) and first-order Takagi-Sugeno-type adaptive neuro-fuzzy inference systems (ANFIS) are utilized to model the fluidized bed-drying process of Echium amoenum Fisch. & C. A. Mey. The moisture ratio evolution is calculated based on the drying temperature, airflow velocity and process time. Different ANN topologies are examined by evaluating the number of neurons (3 to 20), the activation functions and the addition of a second hidden layer. Different numbers (2 to 5) and shapes of membership functions are examined for the ANFIS, using the grid partitioning method. The models with the best performance in terms of prediction accuracy, as evaluated by the statistical indices, are compared with the best fit thin-layer model and the available data from the experimental cases of 40 °C, 50 °C and 60 °C temperatures at 0.5, 0.75 and 1 ms airflow velocity.