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, Türkiye, 30 Mayıs - 01 Haziran 2009, ss.73-78 identifier

  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.73-78


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.