Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › глава/раздел › научная › Рецензирование
Machine Learning-Based Cyber-Physical Systems for Forecasting Short-Term State of Unstable Systems. / Мусаев, Александр Азерович; Григорьев, Дмитрий Алексеевич.
Cyber-Physical Systems: Intelligent Models and Algorithms. Springer Nature, 2022. стр. 189-200 (Studies in Systems, Decision and Control; Том 417).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › глава/раздел › научная › Рецензирование
}
TY - CHAP
T1 - Machine Learning-Based Cyber-Physical Systems for Forecasting Short-Term State of Unstable Systems
AU - Мусаев, Александр Азерович
AU - Григорьев, Дмитрий Алексеевич
N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - We consider the task of developing algorithms for cyber-physical systems (CPS) for proactively managing the state of unstable systems with a chaotically evolving state vector. Examples of such processes are changes in the state of gas- and hydrodynamic environments, stock price evolution, thermal phenomena, and so on. The main problem of this type of CPS is creating forecasts that would allow us to compare the efficiency of different feasible control actions. The presence of a chaotic element in the state dynamics of unstable systems does not allow to build of control CPS based on conventional statistical extrapolation algorithms. Hence, in the current chapter, we consider forecasting algorithms built upon machine learning and instance-based data analysis. In the conditions of chaotic influences, which are common in unstable immersion environments, obtaining an accurate forecast is highly complicated. Within the conducted computational experiment that employed direct averaging by three after-effects of analog windows, the average forecast accuracy oscillates between 15 and 20%. Effective forecasting of a chaotic process of a complicated inertia-less nature based on the considered computational schemes has not been achieved yet. This means that additional research, based on multidimensional statistical measures, is required.
AB - We consider the task of developing algorithms for cyber-physical systems (CPS) for proactively managing the state of unstable systems with a chaotically evolving state vector. Examples of such processes are changes in the state of gas- and hydrodynamic environments, stock price evolution, thermal phenomena, and so on. The main problem of this type of CPS is creating forecasts that would allow us to compare the efficiency of different feasible control actions. The presence of a chaotic element in the state dynamics of unstable systems does not allow to build of control CPS based on conventional statistical extrapolation algorithms. Hence, in the current chapter, we consider forecasting algorithms built upon machine learning and instance-based data analysis. In the conditions of chaotic influences, which are common in unstable immersion environments, obtaining an accurate forecast is highly complicated. Within the conducted computational experiment that employed direct averaging by three after-effects of analog windows, the average forecast accuracy oscillates between 15 and 20%. Effective forecasting of a chaotic process of a complicated inertia-less nature based on the considered computational schemes has not been achieved yet. This means that additional research, based on multidimensional statistical measures, is required.
KW - Controlling of a chaotic system
KW - Matrix similarity measures
KW - Precedent forecasting
UR - http://www.scopus.com/inward/record.url?scp=85127886353&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/a38c507a-dfbc-3b06-ab52-b83cd01cc549/
U2 - 10.1007/978-3-030-95116-0_16
DO - 10.1007/978-3-030-95116-0_16
M3 - Chapter
SN - 978-3-030-95115-3
T3 - Studies in Systems, Decision and Control
SP - 189
EP - 200
BT - Cyber-Physical Systems: Intelligent Models and Algorithms
PB - Springer Nature
ER -
ID: 94385787