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A randomized stochastic approximation algorithm for self-learning. / Granichin, ON; Izmakova, OA.

In: Automation and Remote Control, Vol. 66, No. 8, 2005, p. 1239-1248.

Research output: Contribution to journal › Article › peer-review

Harvard

Granichin, ON & Izmakova, OA 2005, 'A randomized stochastic approximation algorithm for self-learning', Automation and Remote Control, vol. 66, no. 8, pp. 1239-1248. https://doi.org/10.1007/s10513-005-0165-3

APA

Granichin, ON., & Izmakova, OA. (2005). A randomized stochastic approximation algorithm for self-learning. Automation and Remote Control, 66(8), 1239-1248. https://doi.org/10.1007/s10513-005-0165-3

Vancouver

Granichin ON, Izmakova OA. A randomized stochastic approximation algorithm for self-learning. Automation and Remote Control. 2005;66(8):1239-1248. https://doi.org/10.1007/s10513-005-0165-3

Author

Granichin, ON ; Izmakova, OA. / A randomized stochastic approximation algorithm for self-learning. In: Automation and Remote Control. 2005 ; Vol. 66, No. 8. pp. 1239-1248.

BibTeX

@article{0eb70735a3b245158a26349c9831173a,
title = "A randomized stochastic approximation algorithm for self-learning",
abstract = "A new stochastic approximation algorithm with input perturbation for self-learning is designed with test perturbations and has certain useful properties, such as consistency of estimates tinder almost arbitrary perturbations and preservation of simplicity and performance with the growing size of the state space and increasing number of classes. An example. oil computer-aided modeling of learning is given to illustrate the performance of the algorithm.",
author = "ON Granichin and OA Izmakova",
year = "2005",
doi = "10.1007/s10513-005-0165-3",
language = "Английский",
volume = "66",
pages = "1239--1248",
journal = "Automation and Remote Control",
issn = "0005-1179",
publisher = "МАИК {"}Наука/Интерпериодика{"}",
number = "8",

}

RIS

TY - JOUR

T1 - A randomized stochastic approximation algorithm for self-learning

AU - Granichin, ON

AU - Izmakova, OA

PY - 2005

Y1 - 2005

N2 - A new stochastic approximation algorithm with input perturbation for self-learning is designed with test perturbations and has certain useful properties, such as consistency of estimates tinder almost arbitrary perturbations and preservation of simplicity and performance with the growing size of the state space and increasing number of classes. An example. oil computer-aided modeling of learning is given to illustrate the performance of the algorithm.

AB - A new stochastic approximation algorithm with input perturbation for self-learning is designed with test perturbations and has certain useful properties, such as consistency of estimates tinder almost arbitrary perturbations and preservation of simplicity and performance with the growing size of the state space and increasing number of classes. An example. oil computer-aided modeling of learning is given to illustrate the performance of the algorithm.

U2 - 10.1007/s10513-005-0165-3

DO - 10.1007/s10513-005-0165-3

M3 - статья

VL - 66

SP - 1239

EP - 1248

JO - Automation and Remote Control

JF - Automation and Remote Control

SN - 0005-1179

IS - 8

ER -

ID: 5014772