An Examination of Aircraft Aerodynamic Estimation Using Neural Networks

Document Number: 952036

Date Published: September 1995

Author(s):
Joseph J. Totah - NASA Ames Research Center

Abstract:
The aerodynamic stability and control derivative database for the F-15 ACTIVE aircrafts' six degree-of-freedom simulation is currently being modeled using neural networks. The objective is to develop pre-trained neural networks using this database, and upon achieving acceptable levels of size and accuracy, to install the neural networks on the F-15 ACTIVE aircraft for in-flight experimentation in on-line learning and reconfigurable flight controls. The material presented in this paper examines a representative subset of the entire aerodynamic stability and control derivative database in order to: 1) develop accuracy criteria that neural networks must achieve in order to accurately model the database, and 2) develop guidelines for pre-training that will help achieve the accuracies while minimizing network size. The results show that neural networks must be within \mP3.77%, \mP15%, or \mP50%. Depending on individual derivative sensitivities and relative importance rankings. Results also indicate that overall network size requirements can be reduced by 70% without significantly impacting accuracy by modeling several derivatives at once, rather than individually

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See other papers presented at Aerotech Conference & Exposition, September 1995, Los Angeles, CA, USA, Session: Aerotech Conference & Exposition

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