The fitting method for initial iteration firing data is investigated. Firstly, A six-degree of freedom model for depleted shutdown launch vehicle is established in the paper. Furthermore, a fitting method based on Radial basis function Neural Network (RBF-NN) is proposed in order to solve problem of the computing for Initial Iteration Firing Data. Large amounts of training data for RBF-NN, which include the geo-location information-latitude and longitude of launch and target sites- and the corresponding firing data, are generated from off-line vehicle’s trajectory simulations. A mapping network of geo-location information to initial iteration firing data is obtained through RBF-NN. The network can guarantee the high precision of impact point error, and at the same time, decrease the data storage space and shorten the computation time largely. The simulations indicate the effectiveness of the method raised in the article.


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    Title :

    Fitting Initial Firing Data of Launch Vehicle Based on Radial Basis Function Neural Network


    Contributors:
    Zou, Ting (author) / Zhou, Rui (author) / Zhang, Jie (author)


    Publication date :

    2018-08-01


    Size :

    250326 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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