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Variance analysis of identified linear MISO models having spatially correlated inputs, with application to parallel Hammerstein models
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9368-3079
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3672-5316
2014 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 6, 1675-1683 p.Article in journal (Refereed) Published
Abstract [en]

This contribution concerns variance analysis of linear multi-input single-output models when the inputs are temporally white but where different inputs may be correlated. An expression is provided for the variance of a linearly parametrized estimate of the frequency response function from one block, i.e. from one input to the output. In particular, this expression reveals that the variance increases in one block when the number of estimated parameters in another block is increased, but levels off when the number of parameters in the other block reaches the number of parameters in the block in question. It also quantifies exactly how correlation between inputs affects the resulting accuracy and a graphical representation is provided for this purpose. The results are applicable to parallel MISO Hammerstein models when the nonlinearities are known and generalize an existing variance expression for this type of model.

Place, publisher, year, edition, pages
2014. Vol. 50, no 6, 1675-1683 p.
Keyword [en]
System identification, Asymptotic variance, Parallel MISO Hammerstein models, Linear MISO models, Bayesian networks
National Category
Control Engineering Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-148633DOI: 10.1016/j.automatica.2014.04.014ISI: 000338600900015Scopus ID: 2-s2.0-84902151383OAI: oai:DiVA.org:kth-148633DiVA: diva2:737274
Funder
EU, European Research Council, 267381Swedish Research Council, 621-2009-4017
Note

QC 20140812

Available from: 2014-08-12 Created: 2014-08-11 Last updated: 2017-12-05Bibliographically approved

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Hjalmarsson, HåkanMårtensson, Jonas

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