In order to carry out aircraft structural load safety monitoring and accumulate relevant structural load data for aircraft fatigue life assessment, it is necessary to establish aircraft structural load model related to flight parameters. For the nonlinear relationship between aircraft structural loads and flight parameters, the sequential minimal optimization (SMO) algorithm with improved stopping criterion and the particle swarm optimization algorithm are used to improve the support vector machine regression method, and the flight parameters involved in the modeling are selected by the method of flight dynamics analysis combined with the Pearson correlation coefficient. Taking the transonic pitching maneuver of an aircraft as an example, a structural shear model of a wing is established, and the modeling method is verified by simulation. The results show that the accuracy of improved support vector machine regression method is better than the original method. It is concluded that the improved support vector machine regression method can improve the accuracy and generalization ability of the established model.


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    Title :

    Nonlinear Aircraft Structure Load Model Based on Improved Support Vector Machine Regression


    Contributors:
    TANG Ning (author) / BAI Xue (author)


    Publication date :

    2020




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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