Approximate Bayesian Prediction Using State Space Model with Uniform Noise
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Approximate Bayesian Prediction Using State Space Model with Uniform Noise
Original language description
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.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-15970S" target="_blank" >GA18-15970S: Optimal Distributional Design for External Stochastic Knowledge Processing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
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
Number of pages of the result
17
Pages from-to
552-568
Number of pages of the book
570
Publisher name
Springer
Place of publication
Cham
UT code for WoS chapter
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