New approach for the identification and validation of a nonlinear F/A-18 model by use of neural networks.
This paper presents a new approach for identifying and validating the F/A-18 aeroservoelastic model, based on flight flutter tests. The neural network (NN), trained with five different flight flutter cases, is validated using 11 other flight flutter test (FFT) data. A total of 16 FFT cases were obtained for all three flight regimes (subsonic, transonic, and supersonic) at Mach numbers ranging between 0.85 and 1.30 and at altitudes of between 5000 and 25 000 ft. The results obtained highlight the efficiency of the multilayer perceptron NN in model identification. Optimization of the NN requires mixing of two proprieties: the hidden layer size reduction and four-layered NN performances. This paper shows that a four-layer NN with only 16 neurons is enough to create an accurate model. The fit coefficients were higher than 92% for both the identification and the validation test data, thus demonstrating accuracy of the NN.
Boely
Nicolas
N
Laboratory of Applied Research in Active Controls, Avionics and Aeroservoelasticity, Ècole de Technologie Supérieure, University of Quebec, Montreal, QC, Canada. nicolas.boely.1@etsmtl.ca
Botez
Ruxandra Mihaela
RM
D000401:Aircraft / Q000592:standards*; D000465:Algorithms; D001185:Artificial Intelligence; D001359:Aviation / Q000379:methods*; D004548:Elasticity; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D012986:Software Validation; D013314:Stress, Mechanical