Maximum Likelihood identification of Wiener-Hammerstein system with process noise
Paper in proceedings, 2018

The Wiener-Hammerstein model is a block-oriented model consisting of two linear blocks and a static nonlinearity in the middle. We address the identification problem of this model, when a disturbance affects the input of the non-linearity, i.e. process noise. For this case, a Maximum Likelihood estimator is derived, which delivers a consistent estimate of the model parameters. In the presence of process noise, in fact, a standard Prediction Error Method normally leads to biased results. The Maximum Likelihood estimate is then used together with the Best Linear Approximation of the system, in order to implement a complete identification scheme when the parametrization of the linear blocks is not known a priori. The computation of the likelihood function requires numerical integration, which is solved by Monte Carlo and Metropolis-Hastings techniques. Numerical examples show the effectiveness of the identification scheme.


Giuseppe Giordano

Chalmers, Electrical Engineering, Systems and control, Mechatronics

Jonas Sjöberg

Chalmers, Electrical Engineering, Systems and control, Mechatronics

18th IFAC Symposium on System Identification
Stockholm, ,

Areas of Advance

Information and Communication Technology

Subject Categories

Computational Mathematics

Probability Theory and Statistics

Control Engineering



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