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Identication of Stochastic Nonlinear Models Using Optimal Estimating Functions
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). (System Identification)ORCID iD: 0000-0001-5474-7060
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). (System Identification)ORCID iD: 0000-0002-9368-3079
(English)Manuscript (preprint) (Other academic)
Abstract [en]

The first part of the paper examines the asymptotic properties of linear prediction error method estimators, which were recently suggested for the identification of nonlinear stochastic dynamical models. It is shown that their accuracy depends not only on the shape of the unknown distribution of the data, but also on how the model is parameterized. Therefore, it is not obvious in general which linear prediction error method should be preferred. In the second part, the estimating functions approach is introduced and used to construct estimators that are asymptotically optimal with respect to a specific class of estimators. These estimators rely on a partial probabilistic parametric models, and therefore neither require the computations of the likelihood function nor any marginalization integrals. The convergence and consistency of the proposed estimators are established under standard regularity and identifiability assumptions akin to those of prediction error methods. The paper is concluded by several numerical simulation examples.

Keywords [en]
System identication; Parameter Estimation; Stochastic systems; Nonlinear models; Prediction error methods.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-266779OAI: oai:DiVA.org:kth-266779DiVA, id: diva2:1387331
Funder
Swedish Research Council, 2015-05285Swedish Research Council, 2016-06079
Note

This paper is currently under review for possible publication as a regula paper in Automatica. QCR 20200121

Available from: 2020-01-21 Created: 2020-01-21 Last updated: 2020-01-31Bibliographically approved

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