Paper Info
Title
A -Norm-Based Fuzzy Approach to the Estimation of Measurement Uncertainty
Abstract
From a metrological point of view, a measurement process rarely consists of a direct measurement but, rather, of digital signal processing (DSP) performed by one or more instruments. The measurement algorithm makes the numerical results available as functions of acquired samples from input signals. Moreover, when repeated direct measurements are performed, one may speak about interactions in subsequent results (and it may be dependent on the type of instrument being used). With mathematical formalism, the complex relations involved can be described, although again, an indirect measurement result would be obtained. Regardless, no matter what kind of process is being examined, the distribution of the uncertainty associated with the measurement needs to be known. To express a measurement result with its associated uncertainty, the recommendations of the ISO Guide need to be met. Many published papers have proposed the use of fuzzy intervals to describe both the systematic and statistical effects of repeated measurements on the distribution of their results. In this paper, we use a random-fuzzy model, the single measure is represented as a fuzzy set, and the propagation of the possibility distribution through the DSP stage (which simply consists of an average operation) is performed using the extension principle of Zadeh based on a particular triangular norm: the so-called Dombi's.
Year
DOI
Venue
2009
10.1109/TIM.2008.2003339
IEEE Transactions on Instrumentation and Measurement
Keywords
Field
DocType
digital signal processing chips,fuzzy set theory,measurement uncertainty,random processes,DSP,digital signal processing,mathematical formalism,measurement uncertainty estimation,random-fuzzy model,t-norm-based fuzzy approach,triangular norm,Distribution of measurement results,measurement uncertainty,random–fuzzy model,random–fuzzy model
T-norm,Random variable,Digital signal processing,Fuzzy logic,Stochastic process,Measurement uncertainty,Algorithm,Fuzzy set,Control engineering,Fuzzy control system,Mathematics
Journal
Volume
Issue
ISSN
58
2
0018-9456
Citations 
PageRank 
References 
3
0.68
4
Authors
2
Name
Order
Citations
PageRank
Claudio De Capua1228.09
Emilia Romeo292.81