• DocumentCode
    2892751
  • Title

    Neural-Network Based Modelling Approach for Loading Systems

  • Author

    Rövid, András ; Varlaki, Peter ; Orban, Gabriella ; Vadvari, Tibor

  • Author_Institution
    John von Neumann Fac. of Infomatics, Obuda Univ., Budapest, Hungary
  • fYear
    2011
  • fDate
    18-20 Nov. 2011
  • Firstpage
    72
  • Lastpage
    76
  • Abstract
    As in many fields in modern logistics the system modelling and identification play an important role. By the modelling of complex non-linear systems different model approximation approaches are utilized. The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a higher order singular value decomposition (HOSVD) based approximation approach for neural network (NN) model approximation is introduced. The approach will be detailed from the point of view of logistic systems but it may be applicable for other fields, as well. The NNs in this case stand for local models based on which a more complex parameter varying model can numerically be reconstructed and reduced using the HOSVD.
  • Keywords
    approximation theory; loading; logistics; neural nets; nonlinear control systems; singular value decomposition; HOSVD; approximation method; complex nonlinear system; complex parameter varying model; higher order singular value decomposition based approximation approach; loading system; neural-network based modelling approach; system identification; system modelling; Artificial neural networks; Load modeling; Loading; Numerical models; Tensile stress; Vehicles; loading systems; modelling; neural network; system identification; tensor-product;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Emerging Trends in Engineering and Technology (ICETET), 2011 4th International Conference on
  • Conference_Location
    Port Louis
  • ISSN
    2157-0477
  • Print_ISBN
    978-1-4577-1847-2
  • Type

    conf

  • DOI
    10.1109/ICETET.2011.69
  • Filename
    6120557