| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0378658 |
| utime |
20240103201024.4 |
| mtime |
20120828235959.9 |
| DOI |
10.3182/20120711-3-BE-2027.00104 |
| title
(primary) (eng) |
Approximate Bayesian Recursive Estimation of Linear Model with Uniform Noise |
| specification |
| page_count |
5 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0379871 |
| ISBN |
978-3-902823-06-9 |
| title
|
Proceedings of the 16th IFAC Symposium on System Identification |
| page_num |
1803-1807 |
| publisher |
| place |
Brussels |
| name |
IFAC |
| year |
2012 |
|
|
| keyword |
recursive parameter estimation |
| keyword |
bounded noise |
| keyword |
Bayesian learning |
| keyword |
autoregressive models |
| author
(primary) |
| ARLID |
cav_un_auth*0101175 |
| name1 |
Pavelková |
| name2 |
Lenka |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101124 |
| name1 |
Kárný |
| name2 |
Miroslav |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
TA01030123 |
| agency |
GA TA ČR |
| ARLID |
cav_un_auth*0271776 |
|
| abstract
(eng) |
Recursive estimation forms core of adaptive prediction and control. Dynamic exponential family is the only but narrow class of parametric models that allows exact Bayesian estimation. The paper provides an approximate estimation of important autoregressive model with exogenous variables (ARX) and uniform noise. This model reflects well physical nature of modelled system: majority of signals, noise and estimated parameters are bounded. Unlike former solutions, the paper proposes an algorithm that provides a full (approximate) posterior probability density function (pdf) of unknown parameters. Behaviour of the designed algorithm is illustrated by simulations. |
| action |
| ARLID |
cav_un_auth*0282287 |
| name |
16th IFAC Symposium on System Identification The International Federation of Automatic Control |
| place |
Brussels |
| dates |
11.07.2012-13.07.2012 |
| country |
BE |
|
| reportyear |
2013 |
| RIV |
BC |
| num_of_auth |
2 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0210073 |
| arlyear |
2012 |
| mrcbU63 |
cav_un_epca*0379871 Proceedings of the 16th IFAC Symposium on System Identification 978-3-902823-06-9 1803 1807 Brussels IFAC 2012 |
|