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Geometric analysis of stochastic model errors in system identificationPrimeFaces.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)Doctoral thesis, comprehensive summary (Other scientific)
##### Abstract [en]

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

Stockholm: KTH , 2007. , viii, 58, 201-208 p.
##### Series

Trita-EE, ISSN 1653-5146 ; 2007:061
##### Keyword [en]

Automatic Control
##### National Category

Control Engineering
##### Identifiers

URN: urn:nbn:se:kth:diva-4506ISBN: 978-91-7178-770-5OAI: oai:DiVA.org:kth-4506DiVA: diva2:12601
##### Public defence

2007-10-31, Hörsal F3, Lindstedtsvägen 26, Stockholm, 10:00
##### Opponent

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##### Supervisors

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#####

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##### Note

QC 20100810Available from: 2007-10-15 Created: 2007-10-15 Last updated: 2010-08-11Bibliographically approved
##### List of papers

Models of dynamical systems are important in many disciplines of science, ranging from physics and traditional mechanical and electrical engineering to life sciences, computer science and economics. Engineers, for example, use models for development, analysis and control of complex technical systems. Dynamical models can be derived from physical insights, for example some known laws of nature, (which are models themselves), or, as considered here, by fitting unknown model parameters to measurements from an experiment. The latter approach is what we call system identification. A model is always (at best) an approximation of the true system, and for a model to be useful, we need some characterization of how large the model error is. In this thesis we consider model errors originating from stochastic (random) disturbances that the system was subject to during the experiment.

Stochastic model errors, known as variance-errors, are usually analyzed under the assumption of an infinite number of data. In this context the variance-error can be expressed as a (complicated) function of the spectra (and cross-spectra) of the disturbances and the excitation signals, a description of the true system, and the model structure (i.e., the parametrization of the model). The primary contribution of this thesis is an alternative geometric interpretation of this expression. This geometric approach consists in viewing the asymptotic variance as an orthogonal projection on a vector space that to a large extent is defined from the model structure. This approach is useful in several ways. Primarily, it facilitates structural analysis of how, for example, model structure and model order, and possible feedback mechanisms, affect the variance-error. Moreover, simple upper bounds on the variance-error can be obtained, which are independent of the employed model structure.

The accuracy of estimated poles and zeros of linear time-invariant systems can also be analyzed using results closely related to the approach described above. One fundamental conclusion is that the accuracy of estimates of unstable poles and zeros is little affected by the model order, while the accuracy deteriorates fast with the model order for stable poles and zeros. The geometric approach has also shown potential in input design, which treats how the excitation signal (input signal) should be chosen to yield informative experiments. For example, we show cases when the input signal can be chosen so that the variance-error does not depend on the model order or the model structure.

Perhaps the most important contribution of this thesis, and of the geometric approach, is the analysis method as such. Hopefully the methodology presented in this work will be useful in future research on the accuracy of identified models; in particular non-linear models and models with multiple inputs and outputs, for which there are relatively few results at present.

1. A Geometric Approach to Variance Analysis in System Identification: Theory and Nonlinear Systems$(function(){PrimeFaces.cw("OverlayPanel","overlay12596",{id:"formSmash:j_idt423:0:j_idt427",widgetVar:"overlay12596",target:"formSmash:j_idt423:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. A geometric approach to variance analysis in system identification: Linear Time-Invariant Systems$(function(){PrimeFaces.cw("OverlayPanel","overlay12597",{id:"formSmash:j_idt423:1:j_idt427",widgetVar:"overlay12597",target:"formSmash:j_idt423:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Variance error quantification for identified poles and zeros$(function(){PrimeFaces.cw("OverlayPanel","overlay12598",{id:"formSmash:j_idt423:2:j_idt427",widgetVar:"overlay12598",target:"formSmash:j_idt423:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Variance error quantification for identified poles and zeros: Part II. Closed loop identification$(function(){PrimeFaces.cw("OverlayPanel","overlay12599",{id:"formSmash:j_idt423:3:j_idt427",widgetVar:"overlay12599",target:"formSmash:j_idt423:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

5. How to Make Bias and Variance Errors Insensitive to System and Model Complexity in Identification$(function(){PrimeFaces.cw("OverlayPanel","overlay12600",{id:"formSmash:j_idt423:4:j_idt427",widgetVar:"overlay12600",target:"formSmash:j_idt423:4:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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