 |
N. N. Bakhtadze, A. A. Chereshko, V. N. Kushnarev Scenario Forecasting Based on Digital Smart Models of Dynamic Processes |
 |
|
Abstract. The article presents the results of research on the development of intelligent predictive models of dynamic processes in technical, industrial, natural and social systems for several time cycles ahead in conditions of limited uncertainty in changing process parameters. To build models, identification algorithms based on inductive knowledge are used, that is regularities extracted with the help of intellectual analysis methods from the data of the functioning of the process under study. When building models, scenarios for changing the state of processes are formed depending on the potential change in factors. Based on the scenarios, the formation of recommended control actions is carried out, both for automatic control systems and for decision support.
Keywords:
dynamic processes, identification, scenario forecasting, associative search, inductive knowledge.
PP. 70-78.
DOI 10.14357/20718632230308 References
1. Castle, J.L., Clements, M.P. & Hendry, D.F. An Overview of Forecasting Facing Breaks // J Bus Cycle Res – 2016. – 12. С. 3–23. https://doi.org/10.1007/s41549-016-0005-2 2. Саати Т., Кернс К. Аналитическое планирование. Организация систем. – М.: Радио и связь, 1991. – 224 с. 3. Lindgren, M., Bandhold, H. Scenario Planning: An Introductory Overview. // In: Scenario Planning. Palgrave Macmillan, London. – 2009. https://doi.org/10.1057/9780230233584_3 4. Bakhtadze, N., Lototsky, V., Vlasov, S., Sakrutina, E. Associative Search and Wavelet Analysis Techniques in System Identification // Proceedings of the 16th IFAC Symposium on System Identification, – 2012. – P.1227–1232. 5. Moore E. On the reciprocal of the general algebraic matrix // Bulletin of the American Mathematical Society. – 1920; 26. – P. 394–395. 6. Penrose R. A generalized inverse for matrices // Proceedings of the Cambridge Philosophical Society. – 1955. – 51. – P. 406–413. 7. Bakhtadze N., Lototsky V. Knowledge-Based Models of Nonlinear Systems Based on Inductive Learning. – In: New Frontiers in Information and Production Systems Modelling and Analysis Incentive Mechanisms, Competence Management, Knowledge-based Production. –– Springer, Heidelberg. – 016. – P. 85–104. 8. Mallat, S.G. Wavelet Tour of Signal Processing. – Academic Press, san Diego, CA (Barlaud, M. (Ed.)). – 1999. – 635 p. 9. Черешко А.А., Титкина М.С. Применение алгоритмов ассоциативного поиска в системах управления с прогнозирующей моделью // Автоматизация в промышленности. – 2022. – 6. С. 58–62.
|
