Uncertainty modeling of random and systematic errors by means of Monte Carlo and fuzzy techniques
Uncertainty modeling of random and systematic errors by means of Monte Carlo and fuzzy techniques
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DOI：
10.1515/jag.2009.008
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发表时间：
2009-06-01
影响因子：
1.4
通讯作者：
Kutterer, Hansjoerg
中科院分区：
文献类型：
--
作者：
Alkhatib, Hamza;Neumann, Ingo;Kutterer, Hansjoerg
不确定度建模的标准参考是“测量不确定度表达指南（GUM）”。GUM将发生的不确定量分为"A类"和"B类"。"A类"的不确定度是用经典的统计方法确定的，而"B类"则受到其他不确定度的影响，这些不确定度是通过对仪器或测量过程的经验和知识获得的。这两种类型的不确定性可以具有随机和系统误差分量。我们的研究集中在处理和传播不同的不确定性，特别是“B型”的概率和模糊随机方法的详细比较。而概率的方法把所有的不确定性具有随机性，模糊技术之间的随机和确定性的错误区分。在模糊随机方法中，随机分量在随机框架中建模，确定性不确定性通过值域搜索问题处理。应用程序的概述显示的理论和数值例子的不确定性评估中的应用，地面激光扫描（TLS）。
The standard reference in uncertainty modeling is the "Guide to the Expression of Uncertainty in Measurement (GUM)''. GUM groups the occurring uncertain quantities into "Type A'' and "Type B''. Uncertainties of "Type A'' are determined with the classical statistical methods, while "Type B'' is subject to other uncertainties which are obtained by experience and knowledge about an instrument or a measurement process. Both types of uncertainty can have random and systematic error components. Our study focuses on a detailed comparison of probability and fuzzy- random approaches for handling and propagating the different uncertainties, especially those of "Type B''. Whereas a probabilistic approach treats all uncertainties as having a random nature, the fuzzy technique distinguishes between random and deterministic errors. In the fuzzy- random approach the random components are modeled in a stochastic framework, and the deterministic uncertainties are treated by means of a range-of-values search problem. The applied procedure is outlined showing both the theory and a numerical example for the evaluation of uncertainties in an application for terrestrial laserscanning (TLS).