Variance error, interpolation and experiment design

Title: Variance error, interpolation and experiment design
Creator: Mahata, Kaushik
Relation: Automatica Vol. 49, Issue 5, p. 1117-1125
Publisher Link: http://dx.doi.org/10.1016/j.automatica.2013.01.021
Publisher: Pergamon Press
Resource Type: journal article
Date: 2013
Description: We investigate how the variance error associated with the prediction error identification is related to the power spectral densities of the input and the additive noise at the output. Let View the MathML source be the ratio of the input power spectral density (PSD) to the output-noise PSD. We characterize the set of all functions Φ for which the variance error remains constant. This analysis results a minimal, finite-dimensional, affine parameterization of the variance error. This parameterization connects our analysis with the theory of Nevanlinna–Pick interpolation. It is shown that the set of all Φ for which the variance error remains constant can be characterized by the solutions of a Nevanlinna–Pick interpolation problem. This insight has interesting consequences in optimal input design, where it is possible to use some recent tools in analytic interpolation theory to tune shape of the input PSD to suit certain needs.
Subject: system identification; variance error; experiment design; Nevanlinna–Pick interpolation
Identifier: http://hdl.handle.net/1959.13/1300783
Identifier: uon:20145
Identifier: ISSN:0005-1098
Language: eng
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