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Convergence analysis of weighted SPSA-based consensus algorithm in distributed parameter estimation problem. / Sergeenko, Anna; Erofeeva, Victoria; Granichin, Oleg; Granichina, Olga; Proskurnikov, Anton.

в: IFAC-PapersOnLine, Том 54, № 7, 01.07.2021, стр. 126-131.

Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование

Harvard

Sergeenko, A, Erofeeva, V, Granichin, O, Granichina, O & Proskurnikov, A 2021, 'Convergence analysis of weighted SPSA-based consensus algorithm in distributed parameter estimation problem', IFAC-PapersOnLine, Том. 54, № 7, стр. 126-131. https://doi.org/10.1016/j.ifacol.2021.08.346

APA

Sergeenko, A., Erofeeva, V., Granichin, O., Granichina, O., & Proskurnikov, A. (2021). Convergence analysis of weighted SPSA-based consensus algorithm in distributed parameter estimation problem. IFAC-PapersOnLine, 54(7), 126-131. https://doi.org/10.1016/j.ifacol.2021.08.346

Vancouver

Sergeenko A, Erofeeva V, Granichin O, Granichina O, Proskurnikov A. Convergence analysis of weighted SPSA-based consensus algorithm in distributed parameter estimation problem. IFAC-PapersOnLine. 2021 Июль 1;54(7):126-131. https://doi.org/10.1016/j.ifacol.2021.08.346

Author

Sergeenko, Anna ; Erofeeva, Victoria ; Granichin, Oleg ; Granichina, Olga ; Proskurnikov, Anton. / Convergence analysis of weighted SPSA-based consensus algorithm in distributed parameter estimation problem. в: IFAC-PapersOnLine. 2021 ; Том 54, № 7. стр. 126-131.

BibTeX

@article{560097c52e3840df8f36e071af9756ff,
title = "Convergence analysis of weighted SPSA-based consensus algorithm in distributed parameter estimation problem",
abstract = "In this paper, we study a distributed parameter estimation problem in a large-scale network of communication sensors. The goal of the sensors is to find a global estimate of an unknown parameter minimizing, which minimizes some aggregate cost function. Each sensor can communicated to a few “neighbors”, furthermore, the communication channels have limited capacities. To solve the resulting optimization problem, we use a weighted modification of the distributed consensus-based SPSA algorithm whose main advantage over the alternative method is its ability to work in presence of arbitrary unknown-but-bounded noises whose statistical characteristics can be unknown. We provide a convergence analysis of the weighted SPSA-based consensus algorithm and show its efficiency via numerical simulations.",
keywords = "Consensus, Distributed parameter estimation, Randomized algorithms, Sensor network",
author = "Anna Sergeenko and Victoria Erofeeva and Oleg Granichin and Olga Granichina and Anton Proskurnikov",
note = "Publisher Copyright: {\textcopyright} 2021 The Authors.; 19th IFAC Symposium on System Identification, SYSID 2021 ; Conference date: 13-07-2021 Through 16-07-2021",
year = "2021",
month = jul,
day = "1",
doi = "10.1016/j.ifacol.2021.08.346",
language = "English",
volume = "54",
pages = "126--131",
journal = "IFAC-PapersOnLine",
issn = "2405-8971",
publisher = "Elsevier",
number = "7",

}

RIS

TY - JOUR

T1 - Convergence analysis of weighted SPSA-based consensus algorithm in distributed parameter estimation problem

AU - Sergeenko, Anna

AU - Erofeeva, Victoria

AU - Granichin, Oleg

AU - Granichina, Olga

AU - Proskurnikov, Anton

N1 - Publisher Copyright: © 2021 The Authors.

PY - 2021/7/1

Y1 - 2021/7/1

N2 - In this paper, we study a distributed parameter estimation problem in a large-scale network of communication sensors. The goal of the sensors is to find a global estimate of an unknown parameter minimizing, which minimizes some aggregate cost function. Each sensor can communicated to a few “neighbors”, furthermore, the communication channels have limited capacities. To solve the resulting optimization problem, we use a weighted modification of the distributed consensus-based SPSA algorithm whose main advantage over the alternative method is its ability to work in presence of arbitrary unknown-but-bounded noises whose statistical characteristics can be unknown. We provide a convergence analysis of the weighted SPSA-based consensus algorithm and show its efficiency via numerical simulations.

AB - In this paper, we study a distributed parameter estimation problem in a large-scale network of communication sensors. The goal of the sensors is to find a global estimate of an unknown parameter minimizing, which minimizes some aggregate cost function. Each sensor can communicated to a few “neighbors”, furthermore, the communication channels have limited capacities. To solve the resulting optimization problem, we use a weighted modification of the distributed consensus-based SPSA algorithm whose main advantage over the alternative method is its ability to work in presence of arbitrary unknown-but-bounded noises whose statistical characteristics can be unknown. We provide a convergence analysis of the weighted SPSA-based consensus algorithm and show its efficiency via numerical simulations.

KW - Consensus

KW - Distributed parameter estimation

KW - Randomized algorithms

KW - Sensor network

UR - http://www.scopus.com/inward/record.url?scp=85118141649&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/c25e10dd-9d13-31b0-a56f-593dc42a0aef/

U2 - 10.1016/j.ifacol.2021.08.346

DO - 10.1016/j.ifacol.2021.08.346

M3 - Conference article

AN - SCOPUS:85118141649

VL - 54

SP - 126

EP - 131

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8971

IS - 7

T2 - 19th IFAC Symposium on System Identification, SYSID 2021

Y2 - 13 July 2021 through 16 July 2021

ER -

ID: 88777087