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Bayesian kernel-bas...
Bayesian kernel-based system identification with quantized output data
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- Bottegal, Giulio (författare)
- KTH,Reglerteknik,ACCESS Linnaeus Centre
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Pillonetto, G. (författare)
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- Hjalmarsson, Håkan (författare)
- KTH,Reglerteknik,ACCESS Linnaeus Centre
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(creator_code:org_t)
- Elsevier, 2015
- 2015
- Engelska.
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Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 48:28, s. 455-460
- Relaterad länk:
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https://doi.org/10.1...
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visa fler...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- In this paper we introduce a novel method for linear system identification with quantized output data. We model the impulse response as a zero-mean Gaussian process whose covariance (kernel) is given by the recently proposed stable spline kernel, which encodes information on regularity and exponential stability. This serves as a starting point to cast our system identification problem into a Bayesian framework. We employ Markov Chain Monte Carlo (MCMC) methods to provide an estimate of the system. In particular, we show how to design a Gibbs sampler which quickly converges to the target distribution. Numerical simulations show a substantial improvement in the accuracy of the estimates over state-of-the-art kernel-based methods when employed in identification of systems with quantized data.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Nyckelord
- Impulse response
- Linear systems
- Markov processes
- Numerical methods
- Religious buildings
- Bayesian frameworks
- Gibbs samplers
- Identification of systems
- Kernel based methods
- Markov chain Monte Carlo method
- State of the art
- System identification problems
- Zero mean Gaussian process
- Monte Carlo methods
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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