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

authored by

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.

Organisation(s)

Geodetic Institute

External Organisation(s)

University of Texas at El Paso

Type

Contribution to book/anthology

Volume

648

Pages

300-306

No. of pages

7

Publication date

2018

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Control and Systems Engineering, Computer Science(all)

Electronic version(s)

https://doi.org/10.1007/978-3-319-67137-6_34 (Access: Open)

 

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