Comparison of Monte Carlo and Fuzzy Techniques in Uncertainty Modelling
نویسندگان
چکیده
The standard reference in uncertainty modelling 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 like experience with and knowledge about an instrument. Both types of uncertainty can have random and systematic error components. Our study focuses on a critical comparison of Monte Carlo (MC) and Fuzzy techniques in the propagation process of the different uncertainties, especially those of “Type B”. Whereas MC techniques treat all uncertainties as having a random nature, the Fuzzy technique distinguishes between random and systematic errors. The random components are modelled in a stochastic framework, and the systematic uncertainties were treated with Fuzzy techniques. The applied procedure is outlined showing both the theory and a numerical example for the evaluation of uncertainties in an application for laserscanning.
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