• DocumentCode
    2164532
  • Title

    Relative degree of recurrent neural networks

  • Author

    Delgado, A. ; Kambhampati, C.

  • Author_Institution
    Reading Univ., UK
  • fYear
    1994
  • fDate
    5-9 Sep 1994
  • Firstpage
    113
  • Lastpage
    117
  • Abstract
    In the paper some tools from the differential geometry theory of single-input single-output nonlinear systems are applied to a recurrent neural network. It is shown that a change of coordinates and a state feedback can transform a recurrent neural network in to a linear system
  • Keywords
    differential geometry; feedback; nonlinear systems; recurrent neural nets; differential geometry; linear system; recurrent neural networks; relative degree; single-input single-output nonlinear systems; state feedback;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Intelligent Systems Engineering, 1994., Second International Conference on
  • Conference_Location
    Hamburg-Harburg
  • Print_ISBN
    0-85296-621-0
  • Type

    conf

  • DOI
    10.1049/cp:19940611
  • Filename
    332053
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2164532