Details
| Original language | English |
|---|---|
| Title of host publication | Fuzzy Logic in Intelligent System Design |
| Publisher | Springer Verlag |
| Pages | 371-381 |
| Number of pages | 11 |
| Publication status | Published - 30 Sept 2017 |
Publication series
| Name | Advances in Intelligent Systems and Computing |
|---|---|
| Volume | 648 |
| ISSN (Print) | 2194-5357 |
Abstract
Traditional statistical data processing techniques (such as Least Squares) assume that we know the probability distributions of measurement errors. Often, we do not have full information about these distributions. In some cases, all we know is the bound of the measurement error; in such cases, we can use known interval data processing techniques. Sometimes, this bound is fuzzy; in such cases, we can use known fuzzy data processing techniques. However, in many practical situations, we know the probability distribution of the random component of the measurement error and we know the upper bound on the measurement error’s systematic component. For such situations, no general data processing technique is currently known. In this paper, we describe general data processing techniques for such situations, and we show that taking into account interval and fuzzy uncertainty can lead to more adequate statistical estimates.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- General Computer Science
Cite this
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Fuzzy Logic in Intelligent System Design. Springer Verlag, 2017. p. 371-381 (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 - Taking into Account Interval (and Fuzzy) Uncertainty Can Lead to More Adequate Statistical Estimates
AU - Sun, Ligang
AU - Dbouk, Hani
AU - Neumann, Ingo
AU - Schön, Steffen
AU - Kreinovich, Vladik
N1 - Funding information: Acknowledgments. This work was performed when Vladik Kreinovich was a visiting researcher within the Research Training Group “Integrity and collaboration in dynamic sensor networks” at the Geodetic Institute of the Leibniz University of Hannover, a visit supported by the German Science Foundation under grant number GRK2159. This work was also supported in part by NSF grant HRD-1242122.
PY - 2017/9/30
Y1 - 2017/9/30
N2 - Traditional statistical data processing techniques (such as Least Squares) assume that we know the probability distributions of measurement errors. Often, we do not have full information about these distributions. In some cases, all we know is the bound of the measurement error; in such cases, we can use known interval data processing techniques. Sometimes, this bound is fuzzy; in such cases, we can use known fuzzy data processing techniques. However, in many practical situations, we know the probability distribution of the random component of the measurement error and we know the upper bound on the measurement error’s systematic component. For such situations, no general data processing technique is currently known. In this paper, we describe general data processing techniques for such situations, and we show that taking into account interval and fuzzy uncertainty can lead to more adequate statistical estimates.
AB - Traditional statistical data processing techniques (such as Least Squares) assume that we know the probability distributions of measurement errors. Often, we do not have full information about these distributions. In some cases, all we know is the bound of the measurement error; in such cases, we can use known interval data processing techniques. Sometimes, this bound is fuzzy; in such cases, we can use known fuzzy data processing techniques. However, in many practical situations, we know the probability distribution of the random component of the measurement error and we know the upper bound on the measurement error’s systematic component. For such situations, no general data processing technique is currently known. In this paper, we describe general data processing techniques for such situations, and we show that taking into account interval and fuzzy uncertainty can lead to more adequate statistical estimates.
UR - http://www.scopus.com/inward/record.url?scp=85030665317&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67137-6_41
DO - 10.1007/978-3-319-67137-6_41
M3 - Contribution to book/anthology
AN - SCOPUS:85030665317
T3 - Advances in Intelligent Systems and Computing
SP - 371
EP - 381
BT - Fuzzy Logic in Intelligent System Design
PB - Springer Verlag
ER -