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Modeling and identi...
Modeling and identification of uncertain-input systems
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- Risuleo, Riccardo Sven, 1986- (författare)
- KTH,ACCESS Linnaeus Centre
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- Bottegal, Giulio (författare)
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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- Hjalmarsson, Håkan, 1962- (författare)
- KTH,ACCESS Linnaeus Centre
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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. 130-141
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Abstract
Ämnesord
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- We present a new class of models, called uncertain-input models, that allows us to treat system-identification problems in which a linear system is subject to a partially unknown input signal. To encode prior information about the input or the linear system, we use Gaussian-process models. We estimate the model from data using the empirical Bayes approach: the hyperparameters that characterize the Gaussian-process models are estimated from the marginal likelihood of the data. We propose an iterative algorithm to find the hyperparameters that relies on the EM method and results in decoupled update steps. Because in the uncertain-input setting neither the marginal likelihood nor the posterior distribution of the unknowns is tractable, we develop an approximation approach based on variational Bayes. As part of the contribution of the paper, we show that this model structure encompasses many classical problems in system identification such as Hammerstein models, blind system identification, and cascaded linear systems. This connection allows us to build a systematic procedure that applies effectively to all the aforementioned problems, as shown in the numerical simulations presented in the paper.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Estimation algorithms
- Gaussian processes
- Nonlinear models
- Nonparametric identification
- System identification
- Gaussian noise (electronic)
- Identification (control systems)
- Iterative methods
- Linear systems
- Religious buildings
- Blind system identification
- Empirical Bayes approach
- Estimation algorithm
- Non-linear model
- Non-parametric identification
- Posterior distributions
- System identification problems
- Gaussian distribution
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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