• DocumentCode
    1096896
  • Title

    Adaptive RLS algorithms under stochastic excitation-L2 convergence analysis

  • Author

    Bittanti, Sergio ; Camp, Marco

  • Author_Institution
    Dipartimento di Elettronica, Politecnico di Milano, Italy
  • Volume
    36
  • Issue
    8
  • fYear
    1991
  • fDate
    8/1/1991 12:00:00 AM
  • Firstpage
    963
  • Lastpage
    967
  • Abstract
    A very general class of RLS (recursive least squares) algorithms having a forgetting factor is considered. The basic assumptions are that the data generation mechanism is free of disturbances and that the observation vector is a stochastic process satisfying a φ-mixing condition. A stochastic characterization of persistent excitation is first given. Then, it is proved that the algorithm is exponentially convergent in the mean-square sense
  • Keywords
    convergence of numerical methods; filtering and prediction theory; identification; stochastic processes; adaptive RLS algorithms; convergence; forgetting factor; observation vector; recursive least squares; stochastic excitation; stochastic process; Adaptive control; Algorithm design and analysis; Convergence; Input variables; Least squares approximation; Least squares methods; Programmable control; Resonance light scattering; Stochastic processes;
  • fLanguage
    English
  • Journal_Title
    Automatic Control, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9286
  • Type

    jour

  • DOI
    10.1109/9.133189
  • Filename
    133189
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1096896