• DocumentCode
    1231582
  • Title

    The optimum scalar data nonlinearity in LMS adaptation for arbitrary IID inputs

  • Author

    Douglas, Scott C. ; Meng, Teresa H Y

  • Author_Institution
    Inf. Syst. Lab., Stanford Univ., CA, USA
  • Volume
    40
  • Issue
    6
  • fYear
    1992
  • fDate
    6/1/1992 12:00:00 AM
  • Firstpage
    1566
  • Lastpage
    1570
  • Abstract
    The authors show that the optimum nonlinear scale operation upon the elements of the observation vector in the LMS algorithm is exactly x/(1+μx2) for any independent stochastic data input and any noise density. Moreover, use of such a nonlinearity can yield a significant performance improvement in fast adaptation situations
  • Keywords
    least squares approximations; vectors; IID inputs; LMS algorithm; independent stochastic data input; noise density; observation vector; optimum nonlinear scale operation; optimum scalar data nonlinearity; Algorithm design and analysis; Convergence; Echo cancellers; Finite impulse response filter; Least squares approximation; Noise cancellation; Noise generators; Signal processing algorithms; Stochastic processes; Stochastic resonance;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.139261
  • Filename
    139261
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1231582