Identification of Hammerstein nonlinear ARMAX systems

By Feng Ding, Tongwen Chen. at - Automatisierungstechnik, 2005

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Newton iterative identification for a class of output nonlinear systems with moving average noises (2013, methodology): “, c1 = 1 [9, 37].”
Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle (2008, background): “Iterative algorithms are common in the areas of matrix algebra and system identification [1–19].”
Gradient-Based Identification Methods for Hammerstein Nonlinear ARMAX Models (2006, methodology): “The HARMAX model (see Figure 1 in [ 12 ]) may written as”
Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling (2013, methodology): “Some work assume that the linear dynamic block is a finite impulse response (FIR) model [15], an ARX model [13,16], an ARMAX model [12], or an output error model [17].”
Identification methods for Hammerstein nonlinear systems (2011, methodology): “Although the proposed algorithms are developed for Hammerstein ARX systems with white noises, the basic idea can be extended to identify Hammerstein systems with colored noises [24,26], Wiener systems [83,84], or Hammerstein–Wiener systems [27,30].”