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Linear Prediction Error Methods for Stochastic Nonlinear Models
KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik.ORCID-id: 0000-0001-5474-7060
KTH, Skolan för elektroteknik och datavetenskap (EECS), Reglerteknik.ORCID-id: 0000-0002-9368-3079
2019 (engelsk)Inngår i: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 105, s. 49-63Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The estimation problem for stochastic parametric nonlinear dynamical models is recognized to be challenging. The main difficulty is the intractability of the likelihood function and the optimal one-step ahead predictor. In this paper, we present relatively simple prediction error methods based on non-stationary predictors that are linear in the outputs. They can be seen as extensions of the linear identification methods for the case where the hypothesized model is stochastic and nonlinear. The resulting estimators are defined by analytically tractable objective functions in several common cases. It is shown that, under certain identifiability and standard regularity conditions, the estimators are consistent and asymptotically normal. We discuss the relationship between the suggested estimators and those based on second-order equivalent models as well as the maximum likelihood method. The paper is concluded with a numerical simulation example as well as a real-data benchmark problem.

sted, utgiver, år, opplag, sider
Elsevier, 2019. Vol. 105, s. 49-63
Emneord [en]
Parameter estimation; System identification; Stochastic systems; Nonlinear models; Prediction error methods.
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-235340DOI: 10.1016/j.automatica.2019.03.006ISI: 000476963500005Scopus ID: 2-s2.0-85063614946OAI: oai:DiVA.org:kth-235340DiVA, id: diva2:1250170
Forskningsfinansiär
Swedish Research Council, 2015-05285 : 2016-06079
Merknad

QC 20180921

Tilgjengelig fra: 2018-09-21 Laget: 2018-09-21 Sist oppdatert: 2019-08-12bibliografisk kontrollert

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