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Description
  • This paper is concerned with the problem of recursive system identification using nonparametric Gaussian process model. Non-linear stochastic system in consideration is affine in control and given in the input-output form. The use of recursive Gaussian process algorithm for non-linear system identification is proposed to alleviate the computational burden of full Gaussian process. The problem of an online hyper-parameter estimation is handled using proposed ad-hoc procedure. The approach to system identification using recursive Gaussian process is compared with full Gaussian process in terms of model error and uncertainty as well as computational demands. Using Monte Carlo simulations it is shown, that the use of recursive Gaussian process with an ad-hoc learning procedure offers converging estimates of hyper-parameters and constant computational demands.
  • This paper is concerned with the problem of recursive system identification using nonparametric Gaussian process model. Non-linear stochastic system in consideration is affine in control and given in the input-output form. The use of recursive Gaussian process algorithm for non-linear system identification is proposed to alleviate the computational burden of full Gaussian process. The problem of an online hyper-parameter estimation is handled using proposed ad-hoc procedure. The approach to system identification using recursive Gaussian process is compared with full Gaussian process in terms of model error and uncertainty as well as computational demands. Using Monte Carlo simulations it is shown, that the use of recursive Gaussian process with an ad-hoc learning procedure offers converging estimates of hyper-parameters and constant computational demands. (en)
Title
  • Gaussian process based recursive system identification
  • Gaussian process based recursive system identification (en)
skos:prefLabel
  • Gaussian process based recursive system identification
  • Gaussian process based recursive system identification (en)
skos:notation
  • RIV/49777513:23520/14:43923270!RIV15-GA0-23520___
http://linked.open...avai/riv/aktivita
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  • P(ED1.1.00/02.0090), P(GC13-07058J), S
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  • 17960
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  • RIV/49777513:23520/14:43923270
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  • Stochastic systems; Gaussian Process; System Identification; Nonliner Systems (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [85600BC82752]
http://linked.open...v/mistoKonaniAkce
  • Berlin, Germany
http://linked.open...i/riv/mistoVydani
  • Bristol
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  • Journal of Physics: Conference Series
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  • Šimandl, Miroslav
  • Prüher, Jakub
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issn
  • 1742-6588
number of pages
http://bibframe.org/vocab/doi
  • 10.1088/1742-6596/570/1/012002
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  • IOP Publishing
http://localhost/t...ganizacniJednotka
  • 23520
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