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.

Details

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, Allgemeine Computerwissenschaft
Elektronische Version(en): https://doi.org/10.1007/978-3-319-67137-6_34 (Zugang: Geschlossen )
: Details im Forschungsportal „Research@Leibniz University“