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A Geometric Approach to Variance Analysis in System Identification : Theory and Nonlinear Systems
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- Hjalmarsson, Håkan, 1962- (author)
- KTH,Reglerteknik,ACCESS Linnaeus Centre,System Identification Group
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- Mårtensson, Jonas (author)
- KTH,Reglerteknik,ACCESS Linnaeus Centre,System Identification Group
- (creator_code:org_t)
- 2007
- 2007
- English.
- In: PROCEEDINGS OF THE 46TH IEEE CONFERENCE ON DECISION AND CONTRO. - 9781424414970 ; , s. 5092-5097
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Keyword
- 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
- Automatic control
- Reglerteknik
- SRA - ICT
- SRA - Informations- och kommunikationsteknik
Publication and Content Type
- ref (subject category)
- kon (subject category)
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