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Randomized algorithms for stochastic approximation under arbitrary disturbances. / Granichin, O.N.

в: Automation and Remote Control, Том 63, № 2, 02.2002, стр. 209-219.

Результаты исследований: Научные публикации в периодических изданияхстатьяРецензирование

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Granichin, O.N. / Randomized algorithms for stochastic approximation under arbitrary disturbances. в: Automation and Remote Control. 2002 ; Том 63, № 2. стр. 209-219.

BibTeX

@article{9b566ba6449b47f891a4714f635346bb,
title = "Randomized algorithms for stochastic approximation under arbitrary disturbances",
abstract = "New algorithms for stochastic approximation under input disturbance are designed. For the multidimensional case, they are simple in form, generate consistent estimates for unknown parameters under {"}almost arbitrary{"} disturbances, and are easily {"}incorporated{"} in the design of quantum devices for estimating the gradient vector of a function of several variables.",
author = "O.N. Granichin",
year = "2002",
month = feb,
doi = "10.1023/A:1014291407082",
language = "Английский",
volume = "63",
pages = "209--219",
journal = "Automation and Remote Control",
issn = "0005-1179",
publisher = "МАИК {"}Наука/Интерпериодика{"}",
number = "2",

}

RIS

TY - JOUR

T1 - Randomized algorithms for stochastic approximation under arbitrary disturbances

AU - Granichin, O.N.

PY - 2002/2

Y1 - 2002/2

N2 - New algorithms for stochastic approximation under input disturbance are designed. For the multidimensional case, they are simple in form, generate consistent estimates for unknown parameters under "almost arbitrary" disturbances, and are easily "incorporated" in the design of quantum devices for estimating the gradient vector of a function of several variables.

AB - New algorithms for stochastic approximation under input disturbance are designed. For the multidimensional case, they are simple in form, generate consistent estimates for unknown parameters under "almost arbitrary" disturbances, and are easily "incorporated" in the design of quantum devices for estimating the gradient vector of a function of several variables.

U2 - 10.1023/A:1014291407082

DO - 10.1023/A:1014291407082

M3 - статья

VL - 63

SP - 209

EP - 219

JO - Automation and Remote Control

JF - Automation and Remote Control

SN - 0005-1179

IS - 2

ER -

ID: 5016591