Gaussian process based recursive system identification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F14%3A43923270" target="_blank" >RIV/49777513:23520/14:43923270 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1088/1742-6596/570/1/012002" target="_blank" >http://dx.doi.org/10.1088/1742-6596/570/1/012002</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1088/1742-6596/570/1/012002" target="_blank" >10.1088/1742-6596/570/1/012002</a>

Result language
angličtina
Original language name
Gaussian process based recursive system identification
Original language 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.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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