• DocumentCode
    554000
  • Title

    Input compensation learning: Modelling dynamical systems

  • Author

    Krause, A.F. ; Durr, V. ; Schack, T. ; Cruse, H.

  • Author_Institution
    Dept. Neurocognition & Action, Univ. of Bielefeld, Bielefeld, Germany
  • Volume
    1
  • fYear
    2011
  • fDate
    26-28 July 2011
  • Firstpage
    464
  • Lastpage
    468
  • Abstract
    A special class of recurrent neural networks, Input Compensation (IC) networks, is applied to model two exemplary dynamical systems, the Van-der-Pol Oscillator and the Figure-Eight pattern. IC-learning results in compact networks that provide insights into the underlying properties of the modelled system.
  • Keywords
    recurrent neural nets; Input compensation learning; Van-der-Pol oscillator; dynamical system; figure-eight pattern; input compensation network; recurrent neural network; Antennas; Biological system modeling; Mathematical model; Oscillators; Training; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2011 Seventh International Conference on
  • Conference_Location
    Shanghai
  • ISSN
    2157-9555
  • Print_ISBN
    978-1-4244-9950-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2011.6022106
  • Filename
    6022106
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=554000