Abstract This paper presents the results of simulation research of an off-line-trained, feedforward neural-network-based state estimator. The investigated system is the mechanical part of an electrical drive characterised by elastic coupling with a working machine, modelled as a dual-mass system. The aim of the research was to find a set of neural network structures giving useful and repeatable results of the estimation. The mechanical resonance frequency of the system has been adopted at the level of 9.3-10.3 Hz. The selected state variables of the mechanical system are load, speed and stiffness torque of the shaft.


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    Title :

    Neural State Estimator for Complex Mechanical Part of Electrical Drive: Neural Network Size and Performance of State Estimation


    Contributors:

    Published in:

    Publication date :

    2018




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English


    Classification :

    BKL:    53.35 Stromrichter, Transformatoren / 53.35$jStromrichter$jTransformatoren / 53.33 Elektrische Maschinen und Antriebe / 53.33$jElektrische Maschinen und Antriebe




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