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Linear Prediction E...
Linear Prediction Error Methods for Stochastic Nonlinear Models
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- Abdalmoaty, Mohamed, 1986- (författare)
- KTH,Reglerteknik,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- (författare)
- KTH,Reglerteknik,Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
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(creator_code:org_t)
- Elsevier, 2019
- 2019
- Engelska.
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 105, s. 49-63
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Parameter estimation; System identification; Stochastic systems; Nonlinear models; Prediction error methods.
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- art (ämneskategori)
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