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Identification of Stochastic Nonlinear Models Using Optimal Estimating Functions
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- Abdalmoaty, Mohamed, 1986- (author)
- KTH,Reglerteknik,System Identification,Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
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- Hjalmarsson, Håkan, 1962- (author)
- KTH,Reglerteknik,System Identification,Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
- (creator_code:org_t)
- Elsevier, 2020
- 2020
- English.
- In: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 119
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- System identication; Parameter Estimation; Stochastic systems; Nonlinear models; Prediction error methods.
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