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.

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  1. Castro-Garcia, Ricardo; Tiels, Koen; Agudelo, Oscar Mauricio; Suykens, Johan A. K.: Hammerstein system identification through best linear approximation inversion and regularisation (2018)
  2. Cerone, Vito; Razza, Valentino; Regruto, Diego: Set-membership errors-in-variables identification of MIMO linear systems (2018)
  3. Giordano, Giuseppe; Gros, Sébastien; Sjöberg, Jonas: An improved method for Wiener-Hammerstein system identification based on the fractional approach (2018)
  4. Wang, Xuehai; Ding, Feng; Liu, Qingsheng; Jiang, Chuntao: The bias compensation based parameter and state estimation for observability canonical state-space models with colored noise (2018)
  5. Zambrano, J.; Sanchis, J.; Herrero, J. M.; Martínez, M.: WH-EA: an evolutionary algorithm for Wiener-Hammerstein system identification (2018)
  6. Li, Meihang; Liu, Ximei; Ding, Feng: The gradient-based iterative estimation algorithms for bilinear systems with autoregressive noise (2017)
  7. Mzyk, Grzegorz; Wachel, Paweł: Kernel-based identification of Wiener-Hammerstein system (2017)
  8. Schoukens, Maarten; Tiels, Koen: Identification of block-oriented nonlinear systems starting from linear approximations: a survey (2017)
  9. Wang, Ziyun; Wang, Yan; Ji, Zhicheng: A novel two-stage estimation algorithm for nonlinear Hammerstein-Wiener systems from noisy input and output data (2017)
  10. Zhang, Xiao; Ding, Feng; Alsaadi, Fuad E.; Hayat, Tasawar: Recursive parameter identification of the dynamical models for bilinear state space systems (2017)
  11. Aissaoui, Borhen; Soltani, Moêz; Chaari, Abdelkader: Subspace identification of Hammerstein model with unified discontinuous nonlinearity (2016)
  12. Katayama, Tohru; Ase, Hajime: Linear approximation and identification of MIMO Wiener-Hammerstein systems (2016)
  13. Ławryńczuk, Maciej: Nonlinear predictive control of dynamic systems represented by Wiener-Hammerstein models (2016)
  14. Falsone, Alessandro; Piroddi, Luigi; Prandini, Maria: A randomized algorithm for nonlinear model structure selection (2015)
  15. Schoukens, Maarten; Marconato, Anna; Pintelon, Rik; Vandersteen, Gerd; Rolain, Yves: Parametric identification of parallel Wiener-Hammerstein systems (2015)
  16. Tiels, Koen; Schoukens, Maarten; Schoukens, Johan: Initial estimates for Wiener-Hammerstein models using phase-coupled multisines (2015)
  17. Schoukens, Maarten; Pintelon, Rik; Rolain, Yves: Identification of Wiener-Hammerstein systems by a nonparametric separation of the best linear approximation (2014)
  18. Shen, Qianyan; Ding, Feng: Iterative estimation methods for Hammerstein controlled autoregressive moving average systems based on the key-term separation principle (2014)
  19. Vanbeylen, Laurent: A fractional approach to identify Wiener-Hammerstein systems (2014)
  20. Westwick, David T.; Schoukens, Johan: Initial estimates of the linear subsystems of Wiener-Hammerstein models (2012)

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