Aerodynamic Identification using Neural Networks

The use of neural networks and efficient identification algorithms in aerodynamic modeling could substantially reduce the time and work effort in going from wind tunnel and flight test data to model. The model is globally differentiable and can be inspected in any way desired. A number of structured and black box sigmoid type neural net models have been identified for mainly the C z aerodynamic coefficient in the region 0 ffi ff 60 ffi , where the aerodynamic coefficients behave highly nonlinear. The estimation data has been directly extracted from an existing aerodatabase for a generic fighter aircraft, that also has been used for validation. All available data has been used for estimation and the data is considered noiseless, so only the approximation properties of the different models are tested. Somewhat surprisingly, it is found that pure black box models with the same number of parameters as structured models utilizing physical insight, often perform better