• DocumentCode
    3700748
  • Title

    Robot dynamics identification via neural network

  • Author

    Alexander A. Dyda;Dmitry A. Oskin;Andrey V. Artemiev

  • Author_Institution
    Department of Automatic and Information Systems, Far Eastern Federal University, 8 Suhanova St., Vladivostok 690950, Russia
  • Volume
    2
  • fYear
    2015
  • Firstpage
    918
  • Lastpage
    923
  • Abstract
    Recurrent neural network (RNN) - based approach to identification of underwater robot (UR) is considered and investigated in the paper. It was shown that RNN models can be successfully trained to nonlinear behaviour of a UR. Experiments carried out with data taken from UR dynamics model also confirmed effectiveness and prospective of the approach considered.
  • Keywords
    "Mathematical model","Robots","Training","Data models","Recurrent neural networks","Testing"
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2015 IEEE 8th International Conference on
  • Print_ISBN
    978-1-4673-8359-2
  • Type

    conf

  • DOI
    10.1109/IDAACS.2015.7341437
  • Filename
    7341437
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3700748