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Идентификация и оптимизация слабо формализованных процессов в классе стационарных LQ моделей

Authors: Filchenko, Dmytro Viktorovych; Nazarenko, Oleksandr Maksymovych;

Идентификация и оптимизация слабо формализованных процессов в классе стационарных LQ моделей

Abstract

Запропоновано алгоритм мультикритеріальної ідентифікації лінійно-квадратичних моделей з відомими та невідомими керуваннями для імітації, прогнозування та оптимізації слабо формалізованих процесів. Розв’язані задачі короткострокового прогнозування та оптимального керування. Чисельний експеримент проведений на реальних статистичних даних динаміки макроекономічних систем. При цитуванні документа, використовуйте посилання http://essuir.sumdu.edu.ua/handle/123456789/8180 Предложен алгоритм мультикритериальной идентификации линейно-квадратических моделей с известными и неизвестными управлениями для имитации, прогнозирования и оптимизации слабо формализованных процессов. Решены задачи краткосрочного прогнозирования и оптимального управления. Числениный эксперимент проведён на реальных статистических данных динамики макроэкономических систем. При цитировании документа, используйте ссылку http://essuir.sumdu.edu.ua/handle/123456789/8180 An algorithm of multicriterion identification of linear-quadratic models with known and unknown controls has been proposed for simulation, forecasting and optimization of weak formalized processes. The problems of short-term forecasting and optimal control have been solved. Numerical experiment has been performed using real statistical data of macroeconomic systems dynamics. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/8180

Country
Ukraine
Related Organizations
Keywords

мультикритериальная идентификация, прогнозирование, слабо формализованные процессы, оптимальное управление

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green