F16 Icing Identification Based on Neural Networks

Abstract

Abstract This study aims at the identification of in-flight wing icing of an F16 aircraft by using neural networks trained with flight data and by observing the changes of parameters affected by icing. In the light of the previous research on in-flight icing, five parameters are assumed to be affected and so identified. In order to obtain training data set for neural network model, F16 aircraft analytical model is simulated in the time-varying manner. With several simulations, the best neural network model of the F16 aircraft is obtained. The applied tests show that neural network model satisfactorily represents iced F16 aircraft. In this research, icing identification based on neural networks is applied for the first time to F16 aircraft.