• DocumentCode
    2622072
  • Title

    An intelligent LMS+F algorithm

  • Author

    Pazaitis, Dimitrios I. ; Constantinides, A.G.

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK
  • fYear
    1996
  • fDate
    24-26 Jun 1996
  • Firstpage
    486
  • Lastpage
    489
  • Abstract
    A new technique for combining the LMS and LMF cost functions is proposed. The resulting stochastic gradient adaptive algorithm uses a time varying mixing parameter to optimise a combination of the above cost functions, taking into consideration the noise statistics. Furthermore, the behaviour of the proposed algorithm is analysed and convergence conditions are established. Simulation results verify the ability of the algorithm to adapt itself to the noise characteristics, illustrate its enhanced performance and support very well the theoretic analysis. The continuous adaptation of the mixing parameter adds flexibility and enables rapid response of the algorithm to non-stationarities
  • Keywords
    adaptive signal processing; convergence of numerical methods; least mean squares methods; noise; parameter estimation; statistical analysis; stochastic processes; LMF cost functions; LMS; convergence; intelligent LMS+F algorithm; mixing parameter; noise characteristics; noise statistics; nonstationarities response; performance; simulation results; stochastic gradient adaptive algorithm; time varying mixing parameter; Adaptive algorithm; Adaptive signal processing; Algorithm design and analysis; Convergence; Cost function; Educational institutions; Least squares approximation; Scholarships; Signal processing algorithms; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Statistical Signal and Array Processing, 1996. Proceedings., 8th IEEE Signal Processing Workshop on (Cat. No.96TB10004
  • Conference_Location
    Corfu
  • Print_ISBN
    0-8186-7576-4
  • Type

    conf

  • DOI
    10.1109/SSAP.1996.534920
  • Filename
    534920