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Algorithms of sequential identification of system component in chaotic processes. / Мусаев, Александр Азерович; Макшанов, Андрей; Григорьев, Дмитрий Алексеевич.

в: International Journal of Dynamics and Control, Том 11, № 5, 31.01.2023, стр. 2566-2579.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Мусаев, АА, Макшанов, А & Григорьев, ДА 2023, 'Algorithms of sequential identification of system component in chaotic processes', International Journal of Dynamics and Control, Том. 11, № 5, стр. 2566-2579. https://doi.org/10.1007/s40435-023-01121-9

APA

Мусаев, А. А., Макшанов, А., & Григорьев, Д. А. (2023). Algorithms of sequential identification of system component in chaotic processes. International Journal of Dynamics and Control, 11(5), 2566-2579. https://doi.org/10.1007/s40435-023-01121-9

Vancouver

Мусаев АА, Макшанов А, Григорьев ДА. Algorithms of sequential identification of system component in chaotic processes. International Journal of Dynamics and Control. 2023 Янв. 31;11(5):2566-2579. https://doi.org/10.1007/s40435-023-01121-9

Author

Мусаев, Александр Азерович ; Макшанов, Андрей ; Григорьев, Дмитрий Алексеевич. / Algorithms of sequential identification of system component in chaotic processes. в: International Journal of Dynamics and Control. 2023 ; Том 11, № 5. стр. 2566-2579.

BibTeX

@article{ca6a1e61b50a40d5adfd0ca587f5d6ab,
title = "Algorithms of sequential identification of system component in chaotic processes",
abstract = "The problem of sequential filtering of a chaotic random process is considered in the context of the general problem of controlling the state of a dynamic object in an unstable immersion environment. In conditions of chaotic dynamics, traditional sequential processing of observations either does not provide the required level of smoothing, or leads to a significant lagging shift of the estimate of the conditional average. The paper provides a numerical analysis of the effectiveness of algorithms for identifying the system component of chaotic processes based on the terminal indicator of management effectiveness. Several filtering algorithms with improved characteristics according to criteria for smoothing quality and control quality indicators based on the system component isolated from noisy observations are proposed.",
keywords = "Management strategies, Sequential filtering, Stochastic chaos, Terminal performance indicators",
author = "Мусаев, {Александр Азерович} and Андрей Макшанов and Григорьев, {Дмитрий Алексеевич}",
year = "2023",
month = jan,
day = "31",
doi = "10.1007/s40435-023-01121-9",
language = "English",
volume = "11",
pages = "2566--2579",
journal = "International Journal of Dynamics and Control",
issn = "2195-268X",
publisher = "Springer Nature",
number = "5",

}

RIS

TY - JOUR

T1 - Algorithms of sequential identification of system component in chaotic processes

AU - Мусаев, Александр Азерович

AU - Макшанов, Андрей

AU - Григорьев, Дмитрий Алексеевич

PY - 2023/1/31

Y1 - 2023/1/31

N2 - The problem of sequential filtering of a chaotic random process is considered in the context of the general problem of controlling the state of a dynamic object in an unstable immersion environment. In conditions of chaotic dynamics, traditional sequential processing of observations either does not provide the required level of smoothing, or leads to a significant lagging shift of the estimate of the conditional average. The paper provides a numerical analysis of the effectiveness of algorithms for identifying the system component of chaotic processes based on the terminal indicator of management effectiveness. Several filtering algorithms with improved characteristics according to criteria for smoothing quality and control quality indicators based on the system component isolated from noisy observations are proposed.

AB - The problem of sequential filtering of a chaotic random process is considered in the context of the general problem of controlling the state of a dynamic object in an unstable immersion environment. In conditions of chaotic dynamics, traditional sequential processing of observations either does not provide the required level of smoothing, or leads to a significant lagging shift of the estimate of the conditional average. The paper provides a numerical analysis of the effectiveness of algorithms for identifying the system component of chaotic processes based on the terminal indicator of management effectiveness. Several filtering algorithms with improved characteristics according to criteria for smoothing quality and control quality indicators based on the system component isolated from noisy observations are proposed.

KW - Management strategies

KW - Sequential filtering

KW - Stochastic chaos

KW - Terminal performance indicators

UR - https://www.mendeley.com/catalogue/6c451094-61b1-36d6-907a-b9f888f9ec59/

U2 - 10.1007/s40435-023-01121-9

DO - 10.1007/s40435-023-01121-9

M3 - Article

VL - 11

SP - 2566

EP - 2579

JO - International Journal of Dynamics and Control

JF - International Journal of Dynamics and Control

SN - 2195-268X

IS - 5

ER -

ID: 102520697