Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ чувствительности, интегрированный с полным факторным экспериментом× | Методология поверхности отклика (RSM)× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство≠ | Process / pipeline | Hypothesis test |
| Год появления≠ | 1990s–2000s (formalized combination) | 1951 |
| Автор метода≠ | Rooted in factorial experimentation (Fisher, 1935) combined with variance-based sensitivity analysis formalized by Saltelli and colleagues (1990s–2000s) | George E. P. Box & K. B. Wilson |
| Тип≠ | Experimental design with factor importance ranking | Second-order polynomial response surface model |
| Основополагающий источник≠ | Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. John Wiley & Sons. ISBN: 978-0470059975 | Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45. link ↗ |
| Другие названия≠ | SA-FFD, full factorial design with sensitivity analysis, factorial-based sensitivity analysis, FFD-SA | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Связанные≠ | 3 | 7 |
| Сводка≠ | Sensitivity analysis-integrated full factorial design combines exhaustive factorial experimentation — where every combination of factor levels is tested — with systematic sensitivity analysis to quantify how much each input factor drives variation in the output response. This hybrid approach provides both reliable effect estimates and a ranked picture of factor importance, guiding engineers and scientists toward the levers that truly matter for system performance. | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. |
| ScholarGateНабор данных ↗ |
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