Subspace-based methods for the identification of linear time-invariant systems
Automatica (Journal of IFAC) - Special issue on trends in system identification
Identification of dynamic errors-in-variables models
Automatica (Journal of IFAC)
Subspace algorithms for the identification of multivarible dynamic errors-in-variables models
Automatica (Journal of IFAC)
Perspectives on errors-in-variables estimation for dynamic systems
Signal Processing
On Consistency of Subspace Methods for System Identification
Automatica (Journal of IFAC)
Consistency of system identification by global total least squares
Automatica (Journal of IFAC)
Brief paper: Maximum likelihood identification of noisy input-output models
Automatica (Journal of IFAC)
Estimating parameters of dynamic errors-in-variables systems with polynomial nonlinearities
WAV'09 Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science
Identifying dynamic systems with polynomial nonlinearities in the errors-in-variables context
WSEAS TRANSACTIONS on SYSTEMS
| Hi-index | 22.15 |
The paper derives a framework suitable to discuss the classical Koopmans-Levin (KL) and maximum likelihood (ML) algorithms to estimate parameters of errors-in-variables linear models in a unified way. Using the capability of the unified approach a new parameter estimation algorithm is presented offering flexibility to ensure acceptable variance in the estimated parameters. The developed algorithm is based on the application of Hankel matrices of variable size and can equally be considered as a generalized version of the KL method (GKL) or as a reduced version of the ML estimation. The methodology applied to derive the GKL algorithm is used to present a straightforward derivation of the subspace identification algorithm.