• DocumentCode
    670286
  • Title

    Convergence properties of an online learning algorithm in neural network models of complex systems

  • Author

    Azarskov, V.N. ; Nikolaienko, S.A. ; Zhiteckii, L.S.

  • Author_Institution
    Aircraft Control Syst. Dept., Nat. Aviation Univ., Kiev, Ukraine
  • fYear
    2013
  • fDate
    15-17 Oct. 2013
  • Firstpage
    89
  • Lastpage
    92
  • Abstract
    Asymptotic behavior of the online gradient algorithm with a constant step size employed for learning in neural network models of nonlinear systems having hidden layer are studied. The sufficient conditions guaranteeing the convergence of this algorithm in the random environment are established.
  • Keywords
    convergence; large-scale systems; learning (artificial intelligence); neural nets; asymptotic behavior; complex systems; constant step size; convergence properties; hidden layer; neural network models; nonlinear systems; online gradient algorithm; random environment; sufficient conditions; Artificial neural networks; Biological neural networks; Conferences; Convergence; Neurons; Unmanned aerial vehicles; convergence; gradient algorithm; learning; neural network; nonlinear model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD), 2013 IEEE 2nd International Conference
  • Conference_Location
    Kiev
  • Print_ISBN
    978-1-4799-3305-1
  • Type

    conf

  • DOI
    10.1109/APUAVD.2013.6705293
  • Filename
    6705293
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=670286