Initialization of nonlinear state-space models applied to the Wiener–Hammerstein benchmark
Journal article, 2012

In this work a new initialization scheme for nonlinear state-space models is applied to the problem of identifying a Wiener–Hammerstein system on the basis of a set of real data. The proposed approach combines ideas from the statistical learning community with classic system identification methods. The results on the benchmark data are discussed and compared to the ones obtained by other related methods.

Wiener–Hammerstein benchmark data

Nonlinear models

System identification

Neural networks

State-space models


A. Marconato

Vrije Universiteit Brussel (VUB)

Jonas Sjöberg

Chalmers, Signals and Systems, Systems and control, Mechatronics

J. Schoukens

Vrije Universiteit Brussel (VUB)

Control Engineering Practice

0967-0661 (ISSN)

Vol. 20 11 1126-1132

Subject Categories

Control Engineering

Signal Processing



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