• DocumentCode
    1728506
  • Title

    A non-quadratic gradient algorithm

  • Author

    Tao, Gang

  • Author_Institution
    Dept. of Electr. Eng., Virginia Univ., Charlottesville, VA, USA
  • Volume
    4
  • fYear
    1994
  • Firstpage
    3608
  • Abstract
    Based on a non-quadratic cost function, a gradient-type algorithm is developed for estimating parameters of a linear model, resulting in a bounded normalized estimation error which converges to zero asymptotically with time and belongs to L1+α for 0<α<1. Preliminary analysis and simulation results show that the new algorithm leads to a faster convergence of the parameter error to small values than the standard gradient algorithm with α=1
  • Keywords
    convergence of numerical methods; error analysis; linear systems; optimisation; parameter estimation; estimation error; linear model; non-quadratic cost function; non-quadratic gradient algorithm; parameter error; Algorithm design and analysis; Analytical models; Convergence; Cost function; Ear; Estimation error; Parameter estimation; Signal design; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
  • Conference_Location
    Lake Buena Vista, FL
  • Print_ISBN
    0-7803-1968-0
  • Type

    conf

  • DOI
    10.1109/CDC.1994.411710
  • Filename
    411710
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1728506