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对于具有任意类型和范围的维度输入模型的基本效果:缩放和轨迹生成
Rik J L Rutjens1, Leah R Band1,2, Matthew D Jones3
1School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom.
PloS one
|October 25, 2023
概括
基本效应方法现在处理维或任意间隔输入. 新的无维灵敏度指数确保了复杂模型中精确的参数重要性排名.
科学领域:
- 计算科学与工程 计算科学与工程
- 敏感性分析和不确定性量化
背景情况:
- 基本效应方法是一种全球灵敏度分析技术.
- 传统上,它仅限于在[0, 1]上无维输入的模型.
研究的目的:
- 扩展基本效应方法用于具有维度,任意间隔或离散输入的模型.
- 提出和评估新的无维度灵敏度指数,以改善参数排名.
主要方法:
- 通过规范化缩放平均值/中位数绝对效应,开发了两种新的无维灵敏度指数.
- 使用9种轨迹生成方法在4个测试函数中测试的索引,包括Penman-Monteith方程.
- 评估了输入缩放的必要性,以获得准确的参数重要性.
主要成果:
- 缩放的基本效应对于各种输入类型的正确参数重要性排名至关重要.
- 使用较小步骤大小的方法通常会产生更准确的灵敏度排名.
- 通过比较两种拟议的灵敏度指数,可以提供更可靠的评估.
- 模拟点分布和差异是轨迹方法的不可靠性能指标.
结论:
- 提出的方法有效地将基本效应分析扩展到更广泛的模型输入类型.
- 输入缩放和仔细选择轨迹生成方法对于可靠的灵敏度分析至关重要.
- 新的指数为识别关键模型参数提供了更准确和更具多功能性的方法.
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