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《测量不确定度表示指南》(GUM)这个技术规范已被广泛认可,且其测量值关联一个不确定度值的建议也被广泛采纳。然而,这个规范遵循一种固有的概率方法,其应用并不总是可行的,且因为一些技术和经济原因,在可行的情况下,其应用也并不是简单直接的。总结了一种更一般化的不确定度评定和表示的随机模糊变量RFVs方法,系统评述了其关键技术与难点,通过实例表明,RFVs方法在非线性测量函数中传递不确定度具有简单高效的特点,最后对该领域的研究扩展提出了两点建议,给出了使用RFVs扩展贝叶斯定理的初步探讨结果。RFVs方法基于数学可能性理论,从GUM及其基本概念和定义出发对GUM方法进行了扩展,具有明显的优势,该方法的广泛应用也证明了其远大的发展前景。
The GUM is widely accepted as a guideline and its recommendation that values associated with a measure of uncertainty are widely adopted. However, this specification follows an inherently probabilistic approach whose application is not always feasible and its application is not straightforward where feasible, for some technical and economic reasons. This paper summarizes a more general method of evaluating and expressing the uncertainty of random variables, and reviews its key technologies and difficulties systematically. It shows that the RFVs method is simple and efficient in transmitting nonlinear uncertainties in nonlinear measurement functions Finally, two suggestions are put forward to extend the research in this field, and the preliminary results of exploring Bayes’ theorem using RFVs are given. Based on the theory of mathematical possibilities, the RFVs method extends the GUM method from the GUM and its basic concepts and definitions, which has obvious advantages. The extensive application of the method also proves its promising prospects.