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
Block-Toeplitz/Hankel Structured Total Least Squares
SIAM Journal on Matrix Analysis and Applications
Brief Identification of nonlinear errors-in-variables models
Automatica (Journal of IFAC)
Consistency of system identification by global total least squares
Automatica (Journal of IFAC)
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This article considers the problem of estimating linear model parameters from noisy measurements. The starting point is the classical approach by Koopmans for linear regression analysis. It is known that concerning the direct application of those early results for process identification, neither the original Koopmans algorithm nor its updated forms called Koopmans-Levin algorithms exhibit maximum-likelihood (ML) parameter estimation. In this article, a new, numerically advanced method is developed to ensure ML property for the parameter estimation, assuming noisy inputs and outputs, respectively.