Details
| Original language | English |
|---|---|
| Title of host publication | Fuzzy Logic in Intelligent System Design |
| Publisher | Springer Verlag |
| Pages | 300-306 |
| Number of pages | 7 |
| Volume | 648 |
| ISBN (Electronic) | 978-3-319-67137-6 |
| ISBN (Print) | 978-3-319-67136-9 |
| Publication status | Published - 2018 |
Publication series
| Name | Advances in Intelligent Systems and Computing |
|---|---|
| Volume | 648 |
| ISSN (Print) | 2194-5357 |
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.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
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Fuzzy Logic in Intelligent System Design. Vol. 648 Springer Verlag, 2018. p. 300-306 (Advances in Intelligent Systems and Computing; Vol. 648).
Research output: Chapter in book/report/conference proceeding › Contribution to book/anthology › Research › peer review
}
TY - CHAP
T1 - Normalization-Invariant Fuzzy Logic Operations Explain Empirical Success of Student Distributions in Describing Measurement Uncertainty
AU - Alkhatib, Hamza
AU - Kargoll, Boris
AU - Neumann, Ingo
AU - Kreinovich, Vladik
N1 - Funding Information: Acknowledgments. This work was performed when Vladik was a visiting researcher with the Geodetic Institute of the Leibniz University of Hannover, a visit supported by the German Science Foundation. This work was also supported in part by NSF grant HRD-1242122. Publisher Copyright: © Springer International Publishing AG 2018. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85030689158&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67137-6_34
DO - 10.1007/978-3-319-67137-6_34
M3 - Contribution to book/anthology
AN - SCOPUS:85030689158
SN - 978-3-319-67136-9
VL - 648
T3 - Advances in Intelligent Systems and Computing
SP - 300
EP - 306
BT - Fuzzy Logic in Intelligent System Design
PB - Springer Verlag
ER -