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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    Titel :

    SSME Parameter Modeling with Neural Networks


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    Aerospace Atlantic Conference & Exposition ; 1994



    Erscheinungsdatum :

    01.04.1994




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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    Neural Networks Analysis on SSME Vibration Data

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