Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Анализ чувствительности с использованием дробного факторного эксперимента× | Методология поверхности отклика (RSM)× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство≠ | Process / pipeline | Hypothesis test |
| Год появления≠ | 1935 (factorial design); 1990s–2000s (systematic SA integration) | 1951 |
| Автор метода≠ | R. A. Fisher (factorial design foundations); combined with sensitivity analysis frameworks developed by A. Saltelli and colleagues | George E. P. Box & K. B. Wilson |
| Тип≠ | Quantitative experimental screening method | Second-order polynomial response surface model |
| Основополагающий источник≠ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130 | 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≠ | FFD sensitivity analysis, fractional factorial sensitivity screening, SA-FFD, screening design sensitivity analysis | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Связанные≠ | 1 | 7 |
| Сводка≠ | Sensitivity analysis with fractional factorial design (SA-FFD) is an experimental screening method that uses a carefully chosen fraction of all possible factor combinations to identify which input variables most strongly influence a system's output. By running only 2^(k-p) experiments instead of a full 2^k factorial, it makes sensitivity ranking feasible when many factors are present. The approach is widely used in engineering, product development, simulation modeling, and process optimization. | 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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