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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F11%3A00364820%21RIV12-AV0-67985556
rdf:type
n12:Vysledek skos:Concept
dcterms:description
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. 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.
dcterms:title
Fully probabilistic control design in an adaptive critic framework Fully probabilistic control design in an adaptive critic framework
skos:prefLabel
Fully probabilistic control design in an adaptive critic framework Fully probabilistic control design in an adaptive critic framework
skos:notation
RIV/67985556:_____/11:00364820!RIV12-AV0-67985556
n12:predkladatel
n13:ico%3A67985556
n4:aktivita
n15:Z n15:P
n4:aktivity
P(GA102/08/0567), Z(AV0Z10750506)
n4:cisloPeriodika
10
n4:dodaniDat
n9:2012
n4:domaciTvurceVysledku
n20:6585256
n4:druhVysledku
n19:J
n4:duvernostUdaju
n14:S
n4:entitaPredkladatele
n6:predkladatel
n4:idSjednocenehoVysledku
200501
n4:idVysledku
RIV/67985556:_____/11:00364820
n4:jazykVysledku
n18:eng
n4:klicovaSlova
Stochastic control design; Fully probabilistic design; Adaptive control; Adaptive critic
n4:klicoveSlovo
n5:Adaptive%20critic n5:Fully%20probabilistic%20design n5:Stochastic%20control%20design n5:Adaptive%20control
n4:kodStatuVydavatele
GB - Spojené království Velké Británie a Severního Irska
n4:kontrolniKodProRIV
[27C9D44110F9]
n4:nazevZdroje
Neural Networks
n4:obor
n17:BC
n4:pocetDomacichTvurcuVysledku
1
n4:pocetTvurcuVysledku
2
n4:projekt
n16:GA102%2F08%2F0567
n4:rokUplatneniVysledku
n9:2011
n4:svazekPeriodika
24
n4:tvurceVysledku
Herzallah, R. Kárný, Miroslav
n4:wos
000297000300012
n4:zamer
n10:AV0Z10750506
s:issn
0893-6080
s:numberOfPages
8
n7:doi
10.1016/j.neunet.2011.06.006