The High Pressure Oxidizer Turbine (HPOT) discharge temperature of the Space Shuttle Main Engine (SSME) was estimated using Radial Basis Function Neural Networks (RBFNN) during the startup transient. Estimation was performed for both nominal engine operation and during simulated input sensor failures. The K-means clustering algorithm was used on the data to determine the location of the basis function centers. The performance of the RBFNN is compared with that of a feedforward neural network trained with the Quickprop learning algorithm.


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    Title :

    SSME Parameter Modeling with Neural Networks


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    Aerospace Atlantic Conference & Exposition ; 1994



    Publication date :

    1994-04-01




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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