Approximate Bayesian Prediction Using State Space Model with Uniform Noise

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00511101" target="_blank" >RIV/67985556:_____/19:00511101 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-31993-9" target="_blank" >http://dx.doi.org/10.1007/978-3-030-31993-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-31993-9" target="_blank" >10.1007/978-3-030-31993-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Approximate Bayesian Prediction Using State Space Model with Uniform Noise

Popis výsledku v původním jazyce

This paper proposes a one-step-ahead Bayesian output predictor for the linear stochastic state space model with uniformly distributed state and output noises. A model with discrete-time inputs,noutputs and states is considered. The model matrices and noise parameters are supposed to be known. Unknown states are estimated using Bayesian approach. A complex polytopic support of posterior probability density function (pdf) is approximated by a parallelotopic set. The state estimation consists of two stages, namely the time and data update including the mentioned approximation. The output prediction is performed as an inter-step between the time update and the data update. The behaviour of the proposed algorithm is illustrated by simulations and compared with Kalman filter.

Název v anglickém jazyce

Approximate Bayesian Prediction Using State Space Model with Uniform Noise

Popis výsledku anglicky

This paper proposes a one-step-ahead Bayesian output predictor for the linear stochastic state space model with uniformly distributed state and output noises. A model with discrete-time inputs,noutputs and states is considered. The model matrices and noise parameters are supposed to be known. Unknown states are estimated using Bayesian approach. A complex polytopic support of posterior probability density function (pdf) is approximated by a parallelotopic set. The state estimation consists of two stages, namely the time and data update including the mentioned approximation. The output prediction is performed as an inter-step between the time update and the data update. The behaviour of the proposed algorithm is illustrated by simulations and compared with Kalman filter.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA18-15970S" target="_blank" >GA18-15970S: Optimální zpracování externí stochastické znalosti vyjádřené pomocí pravděpodobnostních distribucí</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název knihy nebo sborníku

Informatics in Control, Automation and Robotics : 15th International Conference, ICINCO 2018, Porto, Portugal, July 29-31, 2018, Revised Selected Papers

ISBN

978-3-030-31992-2

Počet stran výsledku

17

Strana od-do

552-568

Počet stran knihy

570

Název nakladatele

Springer

Místo vydání

Cham

Kód UT WoS kapitoly

—