• DocumentCode
    3076628
  • Title

    Invariance principles and applications to distributed parameter identification

  • Author

    Yin, G. ; Fitzpatrick, B.G.

  • Author_Institution
    Dept. of Math., Wayne State Univ., Detroit, MI, USA
  • fYear
    1990
  • fDate
    5-7 Dec 1990
  • Firstpage
    3556
  • Abstract
    A nonlinear least squares parameter estimation procedure is discussed. The main objective is to extend previous results in order to obtain certain functional invariance theorems. In particular, weak convergence methods are used to prove an asymptotic normality result and Strassen´s invariance principle is applied to establish a law of the iterated logarithm. Some examples are presented
  • Keywords
    convergence; invariance; least squares approximations; parameter estimation; Strassen´s invariance principle; asymptotic normality; distributed parameter identification; functional invariance theorems; nonlinear least squares parameter estimation; weak convergence methods; Accelerometers; Brain modeling; Convergence; Damping; Design for experiments; Least squares approximation; Least squares methods; Mathematics; Parameter estimation; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 1990., Proceedings of the 29th IEEE Conference on
  • Conference_Location
    Honolulu, HI
  • Type

    conf

  • DOI
    10.1109/CDC.1990.203486
  • Filename
    203486
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3076628