A unified sequential identification structure based on convergence considerations

  • Authors:

  • Daniel Graupe;Eli Fogel

  • Affiliations:

  • Department of Electrical Engineering, Colorado State University, Fort Collins, CO 80523, U.S.A.;Department of Electrical Engineering, Colorado State University, Fort Collins, CO 80523, U.S.A.

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 1976

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Abstract

This paper presents a unified approach to sequential identification methods, via identification-convergence considerations, such that a fixed-structure identifier of stable and unstable processes is derived. It is shown that various identification algorithms can be derived by a proper choice of identifier parameters subject to simple constraints. Specifically, stochastic approximation and sequential learning identification algorithms are shown to be special cases of the above unified sequential structure, as are other algorithms.