Performance analysis of multi-innovation gradient type identification methods

By Feng Ding, Tongwen Chen. at - Automatisierungstechnik, 2007

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Identification methods for Hammerstein nonlinear systems (2011, methodology): “They are the auxiliary model based identification methods [62–68], the hierarchical identification methods [51–53,69, 70], the multi-innovation identification methods [65,67,71–80], the interactive least squares algorithm [81] and the interactive stochastic gradient algorithm [82].”
Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling (2013, methodology): “presented a multi-innovation stochastic gradient (SG) algorithm for linear regression models [22,23], and for systems with scarce measurements [24] and for non-uniformly sampled systems [25], and a partially coupled SG algorithm for non-uniformly sampled-data systems [26]; Liu et al.”
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].”