Wiener-Hammerstein Benchmark

Wiener-Hammerstein Benchmark This paper describes a benchmark for nonlinear system identification. A Wiener-Hammerstein system is selected as test object. In such a structure there is no direct access to the static nonlinearity starting from the measured input/output, because it is sandwiched between two unknown dynamic systems. The signal-to-noise ratio of the measurements is quite high, which puts the focus of the benchmark on the ability to identify the nonlinear behaviour, and not so much on the noise rejection properties. The benchmark is not intended as a competition, but as a tool to compare the possibilities of different methods to deal with this specific nonlinear structure.


References in zbMATH (referenced in 11 articles )

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  1. Katayama, Tohru; Ase, Hajime: Linear approximation and identification of MIMO Wiener-Hammerstein systems (2016)
  2. Ławryńczuk, Maciej: Nonlinear predictive control of dynamic systems represented by Wiener-Hammerstein models (2016)
  3. Falsone, Alessandro; Piroddi, Luigi; Prandini, Maria: A randomized algorithm for nonlinear model structure selection (2015)
  4. Schoukens, Maarten; Marconato, Anna; Pintelon, Rik; Vandersteen, Gerd; Rolain, Yves: Parametric identification of parallel Wiener-Hammerstein systems (2015)
  5. Tiels, Koen; Schoukens, Maarten; Schoukens, Johan: Initial estimates for Wiener-Hammerstein models using phase-coupled multisines (2015)
  6. Shen, Qianyan; Ding, Feng: Iterative estimation methods for Hammerstein controlled autoregressive moving average systems based on the key-term separation principle (2014)
  7. Vanbeylen, Laurent: A fractional approach to identify Wiener-Hammerstein systems (2014)
  8. Westwick, David T.; Schoukens, Johan: Initial estimates of the linear subsystems of Wiener-Hammerstein models (2012)
  9. Farina, Marcello; Piroddi, Luigi: An iterative algorithm for simulation error based identification of polynomial input-output models using multi-step prediction (2010)
  10. Goethals, Ivan; Pelckmans, Kristiaan; Falck, Tillmann; Suykens, Johan A.K.; De Moor, Bart: NARX identification of Hammerstein systems using least-squares support vector machines (2010)
  11. Van Mulders, Anne; Schoukens, Johan; Volckaert, Marnix; Diehl, Moritz: Two nonlinear optimization methods for black box identification compared (2010)