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

  • Authors:

  • Levente Hunyadi;Istvá Vajk

  • Affiliations:

  • Budapest University of Technology and Economics, Department of Automation and Applied Informatics, Budapest, Hungary;Budapest University of Technology and Economics, Department of Automation and Applied Informatics, Budapest, Hungary

  • Venue:
  • WAV'09 Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science
  • Year:
  • 2009

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Abstract

An approach for identifying single-input single-output discrete-time dynamic nonlinear errors-invariables 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 non-linear 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.