An aerodynamic modeling method based on WNN with Partial Least Squares (PLS) is proposed. In the method, PLS first is applied to extract the feature of original aerodynamic data samples, and then the obtained feature is used to establish the WNN aerodynamic model for aircraft stall from flight test data. Simulation results are given to illustrate that the proposed method is effective and feasible.


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    Title :

    Wavelet Neural Network with PLS Feature Extraction and its Application in Aerodynamic Modeling for Aircraft Stall



    Published in:

    Applied Mechanics and Materials ; 602-605 ; 3131-3134


    Publication date :

    2014-08-11


    Size :

    4 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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    Deep-Stall Aerodynamic Characteristics of T-Tail Aircraft

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    Deep-stall aerodynamic characteristics of T-tail aircraft

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