Maximum Likelihood Identification of Wiener Models

Maximum likelihood identification of Wiener models

The Wiener model is a block oriented model having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will in general lead to biased estimates if there is other disturbances present than measurement noise. The implications of Bu...

Maximum Likelihood Identification of Wiener Models, Report no. LiTH-ISY-R-2902

The Wiener model is a block oriented model having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will in general lead to biased estimates if there is other disturbances present than measurement noise. The implications of Bu...

Maximum Likelihood Identification of Wiener Models -- Journal Version, Report no. LiTH-ISY-R-2903

The Wiener model is a block oriented model having a linear dynamic system followed by a static nonlinearity. The dominating approach to estimate the components of this model has been to minimize the error between the simulated and the measured outputs. We show that this will in general lead to biased estimates if there is other disturbances present than measurement noise. The implications of Bu...

Maximum Likelihood Identification of Gaussian Autoregressive Moving Average Models

Maximum likelihood identification of noisy input-output models

This paper deals with the identification of errors–in–variables (EIV) models corrupted by additive and uncorrelated white Gaussian noises when the noise–free input is an arbitrary signal, not necessarily periodic. In particular, a frequency domain maximum likelihood (ML) estimator is proposed. As some other EIV estimators, this method assumes that the ratio of the noise variances is known.