Samara University Repository
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The repository contains dissertations, research articles, educational and methodological materials, monographs, and regulatory documents. The collections cover a wide range of fields: from aerospace technologies, engine engineering, and computer science to history, linguistics, mathematics, and ecology. The archive brings together the scientific heritage of the university, including materials prepared during the period of Samara State Aerospace University and Samara State University.
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| Title: | Разработка регрессионной модели прогнозирования производительности производственной системы |
| Authors: | Печенин В. А. Ковалева А. М. Сербина В. О. |
| Keywords: | correlation analysis digitalization of production discrete manufacturing KPI machine learning production systems productivity forecasting random forest method regression models simulation modeling smart manufacturing concept technical and economic indicators дискретное производство имитационное моделирование концепция умного производства корреляционный анализ машинное обучение метод случайного леса прогнозирование производительности производственные системы регрессионные модели технико-экономические показатели цифровизация производства |
| Issue Date: | 2025 |
| Citation: | Печенин, В. А. Разработка регрессионной модели прогнозирования производительности производственной системы = Development of a regression model for forecasting the productivity of a production system / В. А. Печенин, А. М. Ковалева, В. О. Сербина // Перспективы развития двигателестроения : материалы междунар. науч.-техн. конф. им. Н. Д. Кузнецова (18–20 июня 2025 г.) / Самар. нац. исслед. ун-т им. С. П. Королева (Самар. ун-т) ; редкол.: Е. В. Шахматов, А. И. Ермаков. - Самара : Изд-во Самар. ун-та, 2025. - С. 322-323. |
| Abstract: | The paper discusses the development of a regression model that allows for accurate and interpretable forecasting of production KPIs. The model was trained based on a series of experiments. The proportion of forecasts below a given error threshold on the test sample was 82% of cases. Input features that make the greatest contribution to the forecast results are identified. |
| URI: | http://repo.ssau.ru/jspui/handle/123456789/43318 |
| Appears in Collections: | Перспективы развития двигателестроения |
| File | Size | Format | |
|---|---|---|---|
| 978-5-7883-2191-2_2025-322-323.pdf | 447.4 kB | Adobe PDF | View/Open |
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