Wiener-Hammerstein benchmark with process noise

M. Schoukens, J.M.M.G. Noël

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic


Process noise is already well studied and modeled in the linear time-invariant (LTI) framework. Nonparametric and parametric noise models (Box-Jenkins, ARX, ARMAX) provide good solutions to the LTI process noise problem [1, 2].
Most of the nonlinear modeling approaches only consider additive (colored) noise at the output (see, for instance, the methods listed in [3, 4]), or are restricted to an ARX or ARMAX like noise model (NARX and NARMAX in [5]). Some recent methods consider a more complex noise framework using expectation maximization, particle filter methods, or errors-in-variables approaches [6, 7, 8].
This benchmark presents a Wiener-Hammerstein electronic circuit where the process noise is the dominant noise distortion.
The next sections describe the Wiener-Hammerstein system (Section 2) and describe the data restrictions (Section 3). The test data and the figures of merit that are used in this benchmark are presented in Section 4. Finally, some of the expected challenges during the identification process are listed in Section 5.
Original languageEnglish
Title of host publicationWorkshop on Nonlinear System Identification Benchmarks : April 25-27, 2016, Brussels, Belgium
Place of PublicationBrussels, Belgium
Number of pages5
Publication statusPublished - Apr 2016
Externally publishedYes
Event2016 Workshop on Nonlinear System Identification Benchmarks - Brussels, Belgium
Duration: 25 Apr 201627 Apr 2016


Workshop2016 Workshop on Nonlinear System Identification Benchmarks


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