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. Liu, Qie; Tang, Xinming; Li, Junhao; Zeng, Jianxue; Zhang, Ke; Chai, Yi: Identification of Wiener-Hammerstein models based on variational Bayesian approach in the presence of process noise (2021)
  2. Cerone, Vito; Razza, Valentino; Regruto, Diego: One-shot set-membership identification of generalized Hammerstein-Wiener systems (2020)
  3. Li, Junhong; Zong, Tiancheng; Gu, Juping; Hua, Liang: Parameter estimation of Wiener systems based on the particle swarm iteration and gradient search principle (2020)
  4. Abdalmoaty, Mohamed Rasheed-Hilmy; Hjalmarsson, Håkan: Linear prediction error methods for stochastic nonlinear models (2019)
  5. Castro-Garcia, Ricardo; Agudelo, Oscar Mauricio; Suykens, Johan A. K.: Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification (2019)
  6. Hammar, Karima; Djamah, Tounsia; Bettayeb, Maamar: Nonlinear system identification using fractional Hammerstein-Wiener models (2019)
  7. Mattos, César Lincoln C.; Barreto, Guilherme A.: A stochastic variational framework for recurrent Gaussian processes models (2019)
  8. Pascu, Valentin; Garnier, Hugues; Ljung, Lennart; Janot, Alexandre: Benchmark problems for continuous-time model identification: design aspects, results and perspectives (2019)
  9. Yu, Wen; Pacheco, Mario: Impact of random weights on nonlinear system identification using convolutional neural networks (2019)
  10. Castro-Garcia, Ricardo; Tiels, Koen; Agudelo, Oscar Mauricio; Suykens, Johan A. K.: Hammerstein system identification through best linear approximation inversion and regularisation (2018)
  11. Cerone, Vito; Razza, Valentino; Regruto, Diego: Set-membership errors-in-variables identification of MIMO linear systems (2018)
  12. Giordano, Giuseppe; Gros, Sébastien; Sjöberg, Jonas: An improved method for Wiener-Hammerstein system identification based on the fractional approach (2018)
  13. 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)
  14. Wang, Youqing; Zhang, Ling; Zhao, Yali: Improved closed-loop subspace identification with prior information (2018)
  15. Zambrano, J.; Sanchis, J.; Herrero, J. M.; Martínez, M.: WH-EA: an evolutionary algorithm for Wiener-Hammerstein system identification (2018)
  16. Li, Meihang; Liu, Ximei; Ding, Feng: The gradient-based iterative estimation algorithms for bilinear systems with autoregressive noise (2017)
  17. Meng, Dandan: Recursive least squares and multi-innovation gradient estimation algorithms for bilinear stochastic systems (2017)
  18. Mzyk, Grzegorz; Wachel, Paweł: Kernel-based identification of Wiener-Hammerstein system (2017)
  19. Schoukens, Maarten; Tiels, Koen: Identification of block-oriented nonlinear systems starting from linear approximations: a survey (2017)
  20. Wang, Ziyun; Wang, Yan; Ji, Zhicheng: A novel two-stage estimation algorithm for nonlinear Hammerstein-Wiener systems from noisy input and output data (2017)

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