The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/12950081

Schoukens, Maarten (2020): Parallel Wiener-Hammerstein Time Series. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/12950081.v1

Dataset

• Artificial Intelligence and Image Processing
• Mechanical Engineering
• Electrical and Electronic Engineering
• Other Engineering

Dynamic System, Electronic Circuit, Machine Learning, Nonlinear, System Identification, time series, Wiener-Hammerstein

CC BY-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Maarten Schoukens

A Parallel Wiener-Hammerstein system is a nonlinear dynamical system obtained by connecting multiple Wiener-Hammerstein systems in parallel. Each parallel branch contains a static nonlinearity that is sandwiched in between two linear time-invariant (LTI) blocks. The presence of the two LTI blocks, and the parallel branches results in a problem that is harder to identify. The LTI blocks are realized as active filters while the static nonlinearity is implemented as a diode-resistor electronic circuit.

The provided data was part of a previously published Automatica paper available online at Sciencedirect or as an ArXiv preprint. The Parallel Wiener-Hammerstein system, the measurement setup, and the input signals used are detailed in Section 10 of the aforementioned paper.

This zip-file contains multiple measured input-output time series: a multisine estimation/validation data set, and a multisine and increasing-amplitude test data set. The data is available in the .csv and .mat file format.

• 2020-09-15 first online, published, posted

4TU.ResearchData

.mat .csv

Parametric identification of parallel Wiener–Hammerstein systems

• https://doi.org/10.1016/j.automatica.2014.10.105

Eindhoven University of Technology, Department of Electrical Engineering, Control System group