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Normalization-Invariant Fuzzy Logic Operations Explain Empirical Success of Student Distributions in Describing Measurement Uncertainty

verfasst von
Hamza Alkhatib, Boris Kargoll, Ingo Neumann, Vladik Kreinovich
Abstract

In engineering practice, usually measurement errors are described by normal distributions. However, in some cases, the distribution is heavy-tailed and thus, not normal. In such situations, empirical evidence shows that the Student distributions are most adequate. The corresponding recommendation – based on empirical evidence – is included in the International Organization for Standardization guide. In this paper, we explain this empirical fact by showing that a natural fuzzy-logic-based formalization of commonsense requirements leads exactly to the Student’s distributions.

Organisationseinheit(en)
Geodätisches Institut
Externe Organisation(en)
University of Texas at El Paso
Typ
Beitrag in Buch/Sammelwerk
Band
648
Seiten
300-306
Anzahl der Seiten
7
Publikationsdatum
2018
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Steuerungs- und Systemtechnik, Informatik (insg.)
Elektronische Version(en)
https://doi.org/10.1007/978-3-319-67137-6_34 (Zugang: Offen)
 

Details im Forschungsportal „Research@Leibniz University“