Abstract
In this paper, we give an overview of the problem of identification of a class of block-oriented systems from the view point of the theory of semiparametric estimation. The semiparametric methodology restricts the system parametrization to a finite-dimensional parameter and nonlinear characteristics that run typically through a nonparametric class of univariate functions. A general methodology for identifying semiparametric block-oriented systems is examined. This is explained by a semiparametric version of least squares applied to the multivariate Hammerstein system. The statistical accuracy of the resulting identification algorithms is discussed.
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