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Proactive Algorithms for Stabilizing the State of Technological Processes Based on Metric Machine Learning Algorithms. / Мусаев, Александр Азерович; Макшанов, Андрей; Григорьев, Дмитрий Алексеевич.

2024 International Russian Automation Conference (RusAutoCon). Institute of Electrical and Electronics Engineers Inc., 2024.

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Мусаев, АА, Макшанов, А & Григорьев, ДА 2024, Proactive Algorithms for Stabilizing the State of Technological Processes Based on Metric Machine Learning Algorithms. в 2024 International Russian Automation Conference (RusAutoCon). Institute of Electrical and Electronics Engineers Inc., Международная научно-техническая конференция "Автоматизация", Сочи, Российская Федерация, 8/09/24. https://doi.org/10.1109/rusautocon61949.2024.10694156

APA

Мусаев, А. А., Макшанов, А., & Григорьев, Д. А. (2024). Proactive Algorithms for Stabilizing the State of Technological Processes Based on Metric Machine Learning Algorithms. в 2024 International Russian Automation Conference (RusAutoCon) Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/rusautocon61949.2024.10694156

Vancouver

Мусаев АА, Макшанов А, Григорьев ДА. Proactive Algorithms for Stabilizing the State of Technological Processes Based on Metric Machine Learning Algorithms. в 2024 International Russian Automation Conference (RusAutoCon). Institute of Electrical and Electronics Engineers Inc. 2024 https://doi.org/10.1109/rusautocon61949.2024.10694156

Author

Мусаев, Александр Азерович ; Макшанов, Андрей ; Григорьев, Дмитрий Алексеевич. / Proactive Algorithms for Stabilizing the State of Technological Processes Based on Metric Machine Learning Algorithms. 2024 International Russian Automation Conference (RusAutoCon). Institute of Electrical and Electronics Engineers Inc., 2024.

BibTeX

@inproceedings{7a718746e277451d9f6d406618a7ccb9,
title = "Proactive Algorithms for Stabilizing the State of Technological Processes Based on Metric Machine Learning Algorithms",
abstract = "The task of proactive stabilization of technological processes (TP), the evolution of the state of which is described by non-stationary random processes, is considered. Metric precedent analysis technologies, which belong to the class of machine learning tasks, are proposed as a mathematical tool. The training data used is a data set containing large arrays of retrospective data obtained during the monitoring of the state of the TP during its previous operation. Basic mathematical models of ongoing processes and the algorithmic apparatus of precedent data analysis used are presented. The results of numerical studies of technologies for forecasting the state of non-stationary TPs and the effectiveness of proactive stabilization algorithms built on their basis are given.",
keywords = "chaotic processes, machine learning, modeling and forecasting, multidimensionality, non-stationary technological process, nonlinear processes, precedent analysis",
author = "Мусаев, {Александр Азерович} and Андрей Макшанов and Григорьев, {Дмитрий Алексеевич}",
year = "2024",
month = sep,
day = "8",
doi = "10.1109/rusautocon61949.2024.10694156",
language = "English",
booktitle = "2024 International Russian Automation Conference (RusAutoCon)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = " 2024 International Russian Automation Conference (RusAutoCon), RusAutocon ; Conference date: 08-09-2024 Through 14-09-2024",
url = "https://rusautocon.org/rusautocon2024-rus.html",

}

RIS

TY - GEN

T1 - Proactive Algorithms for Stabilizing the State of Technological Processes Based on Metric Machine Learning Algorithms

AU - Мусаев, Александр Азерович

AU - Макшанов, Андрей

AU - Григорьев, Дмитрий Алексеевич

PY - 2024/9/8

Y1 - 2024/9/8

N2 - The task of proactive stabilization of technological processes (TP), the evolution of the state of which is described by non-stationary random processes, is considered. Metric precedent analysis technologies, which belong to the class of machine learning tasks, are proposed as a mathematical tool. The training data used is a data set containing large arrays of retrospective data obtained during the monitoring of the state of the TP during its previous operation. Basic mathematical models of ongoing processes and the algorithmic apparatus of precedent data analysis used are presented. The results of numerical studies of technologies for forecasting the state of non-stationary TPs and the effectiveness of proactive stabilization algorithms built on their basis are given.

AB - The task of proactive stabilization of technological processes (TP), the evolution of the state of which is described by non-stationary random processes, is considered. Metric precedent analysis technologies, which belong to the class of machine learning tasks, are proposed as a mathematical tool. The training data used is a data set containing large arrays of retrospective data obtained during the monitoring of the state of the TP during its previous operation. Basic mathematical models of ongoing processes and the algorithmic apparatus of precedent data analysis used are presented. The results of numerical studies of technologies for forecasting the state of non-stationary TPs and the effectiveness of proactive stabilization algorithms built on their basis are given.

KW - chaotic processes

KW - machine learning

KW - modeling and forecasting

KW - multidimensionality

KW - non-stationary technological process

KW - nonlinear processes

KW - precedent analysis

UR - https://www.mendeley.com/catalogue/440b0cb1-0e19-3a79-9cc2-39be71a23915/

UR - https://www.mendeley.com/catalogue/440b0cb1-0e19-3a79-9cc2-39be71a23915/

U2 - 10.1109/rusautocon61949.2024.10694156

DO - 10.1109/rusautocon61949.2024.10694156

M3 - Conference contribution

BT - 2024 International Russian Automation Conference (RusAutoCon)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2024 International Russian Automation Conference (RusAutoCon)

Y2 - 8 September 2024 through 14 September 2024

ER -

ID: 125804367