• DocumentCode
    1265209
  • Title

    A note on sampling and parameter estimation in linear stochastic systems

  • Author

    Duncan, T.E. ; Mandl, P. ; Pasik-Duncan, B.

  • Author_Institution
    Dept. of Math., Kansas Univ., Lawrence, KS, USA
  • Volume
    44
  • Issue
    11
  • fYear
    1999
  • fDate
    11/1/1999 12:00:00 AM
  • Firstpage
    2120
  • Lastpage
    2125
  • Abstract
    Numerical differentiation formulas that yield consistent least squares parameter estimates from sampled observations of linear, time invariant higher order systems have been introduced previously by Duncan et al. (1994). The formulas given by Duncan et al. have the same limiting system of equations as in the continuous time case. The formula presented in this note can be characterized as preserving asymptotically a partial integration rule. It leads to limiting equations for the parameter estimates that are different from the continuous case, but they again imply consistency. The numerical differentiation formulas given here can be used for an arbitrary linear system, which is not the case in the previous paper by Duncan et al
  • Keywords
    differentiation; least squares approximations; linear systems; parameter estimation; sampling methods; stochastic systems; consistency; least squares parameter estimates; linear stochastic systems; numerical differentiation formulas; parameter estimation; partial integration rule; sampling; Differential equations; Least squares approximation; Linear systems; Mathematics; Parameter estimation; Random variables; Sampling methods; Stochastic processes; Stochastic systems; Yield estimation;
  • fLanguage
    English
  • Journal_Title
    Automatic Control, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9286
  • Type

    jour

  • DOI
    10.1109/9.802928
  • Filename
    802928