Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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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