Schoukens, Maarten; Noël, Jean-Philippe (2020): Wiener-Hammerstein Process Noise System Time Series. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/12952124.v1

Dataset

x choose version: version 2 - 2020-09-22 (latest)

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

Dynamical System, Electronic Circuit, Machine Learning, Nonlinear, Process Noise, System Identification, Time Series, Wiener-Hammerstein

CC BY-SA 4.0

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

by Maarten Schoukens , Jean-Philippe Noël

The Wiener-Hammerstein system structure is a well-known block-oriented structure. It contains a static nonlinearity that is sandwiched in between two linear time-invariant (LTI) blocks. The presence of the two LTI blocks 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 Wiener-Hammerstein system proposed here as a benchmark contains dominant process noise. The process noise enters the system before the static nonlinearity. Two much less significant noise sources are present in the measurement channels of the input and output.
All the provided files, data and information on the Wiener-Hammerstein system are available for download here together with an in-depth description of the measured input-output time series. This zip-file contains a detailed system description, an example estimation data set, the test data sets, the datasets measured during the past measurement campaign(s), pictures of the measurement setup, and an indicative electrical circuit schematic of the system. It is possible that the actual implemented Wiener-Hammerstein system deviates at some points (resistor values, opamp type) from the electrical circuit provided here. The data is available in the .csv and .mat file format.

• 2020-09-21 first online, published, posted

4TU.ResearchData

.mat .csv

Three Benchmarks Addressing Open Challenges in Nonlinear System Identification

• https://doi.org/10.1016/j.ifacol.2017.08.071

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