The aerodynamic stability and control derivative database for the F-15 ACTIVE aircraft's 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 ±3.77%, ±15%, or ±50%, 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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    Title :

    An Examination of Aircraft Aerodynamic Estimation Using Neural Networks


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    Aerospace Technology Conference and Exposition ; 1995



    Publication date :

    1995-09-01




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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