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A Geometric Approach to Variance Analysis in System Identification: Theory and Nonlinear SystemsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2007 (English)In: PROCEEDINGS OF THE 46TH IEEE CONFERENCE ON DECISION AND CONTRO, 2007, Vol. FrA17.2, 5092-5097 p.Conference paper (Refereed)
##### Abstract [en]

##### Place, publisher, year, edition, pages

2007. Vol. FrA17.2, 5092-5097 p.
##### Series

, IEEE conference on decision and control - proceedings, ISSN 0191-2216
##### Keyword [en]

covariance analysis, covariance matrices, geometry, identification, nonlinear systems, regression analysis, Hammerstein model, asymptotic covariance matrix, dynamic system, geometric approach, nonlinear regression problem, nonlinear system, system identification, variance analysis, Accuracy of identification, Asymptotic variance expressions
##### National Category

Control Engineering
##### Research subject

SRA - ICT
##### Identifiers

URN: urn:nbn:se:kth:diva-7540DOI: 10.1109/CDC.2007.4434584ISI: 000255181701278ScopusID: 2-s2.0-39549096274ISBN: 978-1-4244-1497-0OAI: oai:DiVA.org:kth-7540DiVA: diva2:12596
##### Conference

46th IEEE Conference on Decision and Control New Orleans, LA, DEC 12-14, 2007
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt375",{id:"formSmash:j_idt375",widgetVar:"widget_formSmash_j_idt375",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt381",{id:"formSmash:j_idt381",widgetVar:"widget_formSmash_j_idt381",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt387",{id:"formSmash:j_idt387",widgetVar:"widget_formSmash_j_idt387",multiple:true});
##### Funder

Swedish Research Council, 621-2007-6271
##### Note

QC 20100810.Available from: 2007-10-15 Created: 2007-10-15 Last updated: 2012-01-19Bibliographically approved
##### In thesis

This paper addresses the problem of quantifying the model error ("variance-error") in estimates of dynamic systems. It is shown that, under very general conditions, the asymptotic (in data length) covariance of an estimated system property (represented by a smooth function of estimated system parameters) can be interpreted in terms of an orthogonal projection of a certain function gamma, associated with the property of interest, onto a subspace determined by the model structure and experimental conditions. An explicit method to construct a suitable gamma, in such a way that the individual impacts of model structure, model order and experimental conditions become visible, is presented. The technique is used to derive asymptotic variance expressions for a Hammerstein model and a nonlinear regression problem.

1. Geometric analysis of stochastic model errors in system identification$(function(){PrimeFaces.cw("OverlayPanel","overlay12601",{id:"formSmash:j_idt647:0:j_idt651",widgetVar:"overlay12601",target:"formSmash:j_idt647:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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