Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harnessing Hybrid Random Search Algorithms for Intelligent State Control in Technological Processes. / Мусаев, Андрей Александрович; Григорьев, Дмитрий Алексеевич.
Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference. Vol. 3 Springer Nature, 2024. p. 464-470 (Lecture Notes in Networks and Systems; Vol. 1090).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Harnessing Hybrid Random Search Algorithms for Intelligent State Control in Technological Processes
AU - Мусаев, Андрей Александрович
AU - Григорьев, Дмитрий Алексеевич
PY - 2024
Y1 - 2024
N2 - This article presents a research focused on improving the control of stochastic search procedures within an ε-neighborhood surrounding current monitoring outcomes of control parameters in technological processes. The research aimed to enhance the effectiveness of control measures through the utilization of state-of-the-art Random Search (RS) technology. A mathematical structure was established to define an ε-neighborhood for manipulating parameters and setting boundaries for the search space. Various methods were explored for selecting manipulation parameters using operator-driven processes via Human-Machine Interface (HMI) tools. The RS-Control Module's functional structure accounted for the dynamics of controlled parameter evolution using a sliding observation window. The RS-Optimization Module's software implementation allowed for forecasting output parameters and assessing forecast accuracy using quality indicators. The implementation of the main program provided a user-friendly interface for adjusting process parameters, optimizing criteria, and monitoring control based on RS forecasts. The research demonstrated the effectiveness of the proposed approach in improving output and technological parameters in industrial processes.
AB - This article presents a research focused on improving the control of stochastic search procedures within an ε-neighborhood surrounding current monitoring outcomes of control parameters in technological processes. The research aimed to enhance the effectiveness of control measures through the utilization of state-of-the-art Random Search (RS) technology. A mathematical structure was established to define an ε-neighborhood for manipulating parameters and setting boundaries for the search space. Various methods were explored for selecting manipulation parameters using operator-driven processes via Human-Machine Interface (HMI) tools. The RS-Control Module's functional structure accounted for the dynamics of controlled parameter evolution using a sliding observation window. The RS-Optimization Module's software implementation allowed for forecasting output parameters and assessing forecast accuracy using quality indicators. The implementation of the main program provided a user-friendly interface for adjusting process parameters, optimizing criteria, and monitoring control based on RS forecasts. The research demonstrated the effectiveness of the proposed approach in improving output and technological parameters in industrial processes.
KW - Hybrid algorithms
KW - Predictive algorithms
KW - Random Search Algorithms
KW - Stationarity violation
UR - https://www.mendeley.com/catalogue/48618930-21cc-3a8c-9c7a-f9791077fd7b/
U2 - 10.1007/978-3-031-67192-0_52
DO - 10.1007/978-3-031-67192-0_52
M3 - Conference contribution
SN - 978-3-031-67191-3
VL - 3
T3 - Lecture Notes in Networks and Systems
SP - 464
EP - 470
BT - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference
PB - Springer Nature
Y2 - 16 July 2024 through 18 September 2024
ER -
ID: 123947895