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2016 | OriginalPaper | Buchkapitel

Semiparametric Inference in Identification of Block-Oriented Systems

verfasst von : Mirosław Pawlak

Erschienen in: Challenges in Computational Statistics and Data Mining

Verlag: Springer International Publishing

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Abstract

In this paper, we give the semiparametric statistics perspective on the problem of identification of a class of nonlinear dynamic systems. We present a framework for identification of the so-called block-oriented systems that can be represented by finite-dimensional parameters and an infinite-dimensional set of nonlinear characteristics that run typically through a nonparametric class of univariate functions. We consider systems which are expressible exactly in this form and the case when they are approximative models. In the latter case, we derive projections, that is, solutions which minimize the mean \(L_2\) error. The chief benefit of such an approach is to make classical nonparametric estimates amenable to the incorporation of constraints and able to overcome the celebrated curse of dimensionality and system complexity. The developed methodology is explained by examining semiparametric versions of popular block-oriented structures, i.e., Hammerstein, Wiener, and parallel systems.

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Titel: Semiparametric Inference in Identification of Block-Oriented Systems
verfasst von: Mirosław Pawlak
Verlag: Springer International Publishing
Buch: Challenges in Computational Statistics and Data Mining
Print ISBN: 978-3-319-18780-8

Electronic ISBN: 978-3-319-18781-5

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-18781-5_16

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