Estimating parameters of dynamic errors-in-variables systems with polynomial nonlinearities

Hunyadi L., Vajk I.

8th WSEAS Int Conference on Signal Processing/3rd WSEAS Int Symposium on Wavelets Theory and Applicat in Appl Math, Signal Proc and Modern Sci, İstanbul, Turkey, 30 May - 01 June 2009, pp.73-78 identifier

  • Publication Type: Conference Paper / Full Text
  • City: İstanbul
  • Country: Turkey
  • Page Numbers: pp.73-78
  • Istanbul Technical University Affiliated: No

Abstract

An approach for identifying single-input single-output discrete-time dynamic nonlinear errors-in-variables systems is presented where the system model can be linearized such that it is expressed as a linear combination of polynomials of input and output observations. We assume white Gaussian noise on both input and output, characterized by a noise magnitude and a normalized noise covariance structure matrix, and employ a nonlinear extension of the generalized Koopmans-Levin method to estimate model parameters with an assumed noise structure and a subsequent covariance matching objective function minimization to estimate all noise parameters. The feasibility of the approach is demonstrated by Monte-Carlo simulations.