| bibtype |
J -
Journal Article
|
| ARLID |
0364820 |
| utime |
20240103195601.9 |
| mtime |
20111101235959.9 |
| WOS |
000297000300012 |
| SCOPUS |
80054779788 |
| DOI |
10.1016/j.neunet.2011.06.006 |
| title
(primary) (eng) |
Fully probabilistic control design in an adaptive critic framework |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0257310 |
| ISSN |
0893-6080 |
| title
|
Neural Networks |
| volume_id |
24 |
| volume |
10 (2011) |
| page_num |
1128-1135 |
| publisher |
|
|
| keyword |
Stochastic control design |
| keyword |
Fully probabilistic design |
| keyword |
Adaptive control |
| keyword |
Adaptive critic |
| author
(primary) |
| ARLID |
cav_un_auth*0275438 |
| name1 |
Herzallah |
| name2 |
R. |
| country |
JO |
|
| 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 |
GA102/08/0567 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0239566 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
Optimal stochastic controller pushes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design (FPD) uses probabilistic description of the desired closed loop and minimizes Kullback–Leibler divergence of the closed-loop description to the desired one. Practical exploitation of the fully probabilistic design control theory continues to be hindered by the computational complexities involved in numerically solving the associated stochastic dynamic programming problem; in particular, very hard multivariate integration and an approximate interpolation of the involved multivariate functions. This paper proposes a new fully probabilistic control algorithm that uses the adaptive critic methods to circumvent the need for explicitly evaluating the optimal value function, thereby dramatically reducing computational requirements. This is a main contribution of this paper. |
| reportyear |
2012 |
| RIV |
BC |
| mrcbC52 |
4 A 4a 20231122134701.4 |
| permalink |
http://hdl.handle.net/11104/0200201 |
| mrcbT16-e |
COMPUTERSCIENCEARTIFICIALINTELLIGENCE|NEUROSCIENCES |
| mrcbT16-f |
2.477 |
| mrcbT16-g |
0.318 |
| mrcbT16-h |
9.9 |
| mrcbT16-i |
0.01073 |
| mrcbT16-j |
0.918 |
| mrcbT16-k |
5336 |
| mrcbT16-l |
110 |
| mrcbT16-q |
87 |
| mrcbT16-s |
0.835 |
| mrcbT16-y |
40.73 |
| mrcbT16-x |
3.16 |
| mrcbT16-4 |
Q2 |
| mrcbT16-B |
49.356 |
| mrcbT16-C |
56.771 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q3 |
| arlyear |
2011 |
| mrcbTft |
\nSoubory v repozitáři: karny-0364820.pdf |
| mrcbU14 |
80054779788 SCOPUS |
| mrcbU34 |
000297000300012 WOS |
| mrcbU63 |
cav_un_epca*0257310 Neural Networks 0893-6080 1879-2782 Roč. 24 č. 10 2011 1128 1135 Elsevier |
|