The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.


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    Titel :

    Parameter estimation in space systems using recurrent neural networks


    Beteiligte:

    Kongress:

    AIAA Guidance, Navigation and Control Conference ; 1991 ; New Orleans, LA, United States


    Erscheinungsdatum :

    1991-01-01


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Keine Angabe


    Sprache :

    Englisch


    Schlagwörter :


    Parameter estimation in space systems using recurrent neural networks

    PARLOS, ALEXANDER / ATIYA, AMIR / SUNKEL, JOHN | AIAA | 1991



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