A blind approach to the Hammerstein-Wiener model identification

By E. Bai. at - Automatisierungstechnik, 2002

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On the interpretation and practice of dynamical differences between Hammerstein and Wiener models (2005, methodology): “The method discussed in [ 6 ] does not assume prior knowledge but does assume that the input and output signals are sampled at different rates and that the output is oversampled.”
Identification methods for Hammerstein nonlinear systems (2011, methodology): “The results have been extended to Hammerstein–Wiener systems [22].”
Gradient-Based Identification Methods for Hammerstein Nonlinear ARMAX Models (2006, background): “To the authors’ best knowledge, many publications discussed the identification problem of nonlinear ARX models, or equation-error-like models [6–11], few addressed identification algorithms of Hammerstein systems with colored noises, especially the convergence problem of the algorithms involved, whi”
Identification of Hammerstein nonlinear ARMAX systems (2005, methodology): “…consideration are the nonlinear ARX models, or equation-error-like models (Narendra & Gallman, 1966; Nešić & Mareels, 1998; Wigren & Nordsjö, 1999;Bai, 1998, 2002a,b; Chang & Luus, 1971), and few address parametric model identification methods and their convergence for the Hammerstein nonlinear…”
Newton iterative identification for a class of output nonlinear systems with moving average noises (2013, methodology): “Hammerstein models, Wiener models, and their combinations are the three commonly used nonlinear models [9–13].”