Application of a Linear PEM Estimator to a Stochastic Wiener-Hammerstein Benchmark Problem⁎

• The estimation problem of stochastic Wiener-Hammerstein models is recognized to be challenging, mainly due to the analytical intractability of the likelihood function. In this contribution, we apply a computationally attractive prediction error method estimator to a real-data stochastic Wiener-Hammerstein benchmark problem. The estimator is defined using a deterministic predictor that is nonlinear in the input. The prediction error method results in tractable expressions, and Monte Carlo approximations are not necessary. This allows us to tackle several issues considered challenging from the perspective of the current mainstream approach. Under mild conditions, the estimator can be shown to be consistent and asymptotically normal. The results of the method applied to the benchmark data are presented and discussed.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

Benchmark problem

Nonlinear systems

Stochastic systems

System identification

Wiener-Hammerstein

Error analysis

Identification (control systems)

Monte Carlo methods

Stochastic models

Bench-mark problems

Benchmark data

Estimation problem

Likelihood functions

Monte-carlo approximations

Prediction error method

Wiener-hammerstein models

Benchmarking

Electrical Engineering

Publication and Content Type

ref (subject category)

art (subject category)