Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions
A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ferrero, Alessandro [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on instrumentation and measurement - New York, NY, 1963, 65(2016), 5, Seite 1015-1021 |
|---|---|
| Übergeordnetes Werk: |
volume:65 ; year:2016 ; number:5 ; pages:1015-1021 |
| Links: |
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| DOI / URN: |
10.1109/TIM.2016.2514782 |
|---|
| Katalog-ID: |
OLC1973863251 |
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| 520 | |a A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide to the Expression of Uncertainty in Measurement is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of random-fuzzy variables. Under this approach, nonrandom contributions to uncertainty can also be considered. An experimental example is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach. | ||
| 650 | 4 | |a uncertainty evaluation | |
| 650 | 4 | |a Nonlinear operations | |
| 650 | 4 | |a Uncertainty | |
| 650 | 4 | |a random-fuzzy variables (RFVs) | |
| 650 | 4 | |a Measurement uncertainty | |
| 650 | 4 | |a systematic effects | |
| 650 | 4 | |a Monte Carlo methods | |
| 650 | 4 | |a Yttrium | |
| 650 | 4 | |a possibility distributions (PDs) | |
| 650 | 4 | |a Systematics | |
| 650 | 4 | |a Entropy | |
| 700 | 1 | |a Prioli, Marco |4 oth | |
| 700 | 1 | |a Salicone, Simona |4 oth | |
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10.1109/TIM.2016.2514782 doi PQ20160430 (DE-627)OLC1973863251 (DE-599)GBVOLC1973863251 (PRQ)c940-da341dfb507ecda56ac7bdea5338cfe5352f3894fc4dc3d3e076fc39a1a4688a0 (KEY)0079426020160000065000501015jointrandomfuzzyvariablesatoolforpropagatinguncert DE-627 ger DE-627 rakwb eng 620 DNB 50.21 bkl 53.00 bkl Ferrero, Alessandro verfasserin aut Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide to the Expression of Uncertainty in Measurement is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of random-fuzzy variables. Under this approach, nonrandom contributions to uncertainty can also be considered. An experimental example is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach. uncertainty evaluation Nonlinear operations Uncertainty random-fuzzy variables (RFVs) Measurement uncertainty systematic effects Monte Carlo methods Yttrium possibility distributions (PDs) Systematics Entropy Prioli, Marco oth Salicone, Simona oth Enthalten in IEEE transactions on instrumentation and measurement New York, NY, 1963 65(2016), 5, Seite 1015-1021 (DE-627)129358576 (DE-600)160442-9 (DE-576)014730863 0018-9456 nnns volume:65 year:2016 number:5 pages:1015-1021 http://dx.doi.org/10.1109/TIM.2016.2514782 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7390044 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_24 GBV_ILN_30 GBV_ILN_70 GBV_ILN_170 GBV_ILN_2014 GBV_ILN_2061 50.21 AVZ 53.00 AVZ AR 65 2016 5 1015-1021 |
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10.1109/TIM.2016.2514782 doi PQ20160430 (DE-627)OLC1973863251 (DE-599)GBVOLC1973863251 (PRQ)c940-da341dfb507ecda56ac7bdea5338cfe5352f3894fc4dc3d3e076fc39a1a4688a0 (KEY)0079426020160000065000501015jointrandomfuzzyvariablesatoolforpropagatinguncert DE-627 ger DE-627 rakwb eng 620 DNB 50.21 bkl 53.00 bkl Ferrero, Alessandro verfasserin aut Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide to the Expression of Uncertainty in Measurement is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of random-fuzzy variables. Under this approach, nonrandom contributions to uncertainty can also be considered. An experimental example is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach. uncertainty evaluation Nonlinear operations Uncertainty random-fuzzy variables (RFVs) Measurement uncertainty systematic effects Monte Carlo methods Yttrium possibility distributions (PDs) Systematics Entropy Prioli, Marco oth Salicone, Simona oth Enthalten in IEEE transactions on instrumentation and measurement New York, NY, 1963 65(2016), 5, Seite 1015-1021 (DE-627)129358576 (DE-600)160442-9 (DE-576)014730863 0018-9456 nnns volume:65 year:2016 number:5 pages:1015-1021 http://dx.doi.org/10.1109/TIM.2016.2514782 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7390044 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_24 GBV_ILN_30 GBV_ILN_70 GBV_ILN_170 GBV_ILN_2014 GBV_ILN_2061 50.21 AVZ 53.00 AVZ AR 65 2016 5 1015-1021 |
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10.1109/TIM.2016.2514782 doi PQ20160430 (DE-627)OLC1973863251 (DE-599)GBVOLC1973863251 (PRQ)c940-da341dfb507ecda56ac7bdea5338cfe5352f3894fc4dc3d3e076fc39a1a4688a0 (KEY)0079426020160000065000501015jointrandomfuzzyvariablesatoolforpropagatinguncert DE-627 ger DE-627 rakwb eng 620 DNB 50.21 bkl 53.00 bkl Ferrero, Alessandro verfasserin aut Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide to the Expression of Uncertainty in Measurement is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of random-fuzzy variables. Under this approach, nonrandom contributions to uncertainty can also be considered. An experimental example is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach. uncertainty evaluation Nonlinear operations Uncertainty random-fuzzy variables (RFVs) Measurement uncertainty systematic effects Monte Carlo methods Yttrium possibility distributions (PDs) Systematics Entropy Prioli, Marco oth Salicone, Simona oth Enthalten in IEEE transactions on instrumentation and measurement New York, NY, 1963 65(2016), 5, Seite 1015-1021 (DE-627)129358576 (DE-600)160442-9 (DE-576)014730863 0018-9456 nnns volume:65 year:2016 number:5 pages:1015-1021 http://dx.doi.org/10.1109/TIM.2016.2514782 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7390044 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_24 GBV_ILN_30 GBV_ILN_70 GBV_ILN_170 GBV_ILN_2014 GBV_ILN_2061 50.21 AVZ 53.00 AVZ AR 65 2016 5 1015-1021 |
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10.1109/TIM.2016.2514782 doi PQ20160430 (DE-627)OLC1973863251 (DE-599)GBVOLC1973863251 (PRQ)c940-da341dfb507ecda56ac7bdea5338cfe5352f3894fc4dc3d3e076fc39a1a4688a0 (KEY)0079426020160000065000501015jointrandomfuzzyvariablesatoolforpropagatinguncert DE-627 ger DE-627 rakwb eng 620 DNB 50.21 bkl 53.00 bkl Ferrero, Alessandro verfasserin aut Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide to the Expression of Uncertainty in Measurement is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of random-fuzzy variables. Under this approach, nonrandom contributions to uncertainty can also be considered. An experimental example is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach. uncertainty evaluation Nonlinear operations Uncertainty random-fuzzy variables (RFVs) Measurement uncertainty systematic effects Monte Carlo methods Yttrium possibility distributions (PDs) Systematics Entropy Prioli, Marco oth Salicone, Simona oth Enthalten in IEEE transactions on instrumentation and measurement New York, NY, 1963 65(2016), 5, Seite 1015-1021 (DE-627)129358576 (DE-600)160442-9 (DE-576)014730863 0018-9456 nnns volume:65 year:2016 number:5 pages:1015-1021 http://dx.doi.org/10.1109/TIM.2016.2514782 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7390044 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_24 GBV_ILN_30 GBV_ILN_70 GBV_ILN_170 GBV_ILN_2014 GBV_ILN_2061 50.21 AVZ 53.00 AVZ AR 65 2016 5 1015-1021 |
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10.1109/TIM.2016.2514782 doi PQ20160430 (DE-627)OLC1973863251 (DE-599)GBVOLC1973863251 (PRQ)c940-da341dfb507ecda56ac7bdea5338cfe5352f3894fc4dc3d3e076fc39a1a4688a0 (KEY)0079426020160000065000501015jointrandomfuzzyvariablesatoolforpropagatinguncert DE-627 ger DE-627 rakwb eng 620 DNB 50.21 bkl 53.00 bkl Ferrero, Alessandro verfasserin aut Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide to the Expression of Uncertainty in Measurement is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of random-fuzzy variables. Under this approach, nonrandom contributions to uncertainty can also be considered. An experimental example is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach. uncertainty evaluation Nonlinear operations Uncertainty random-fuzzy variables (RFVs) Measurement uncertainty systematic effects Monte Carlo methods Yttrium possibility distributions (PDs) Systematics Entropy Prioli, Marco oth Salicone, Simona oth Enthalten in IEEE transactions on instrumentation and measurement New York, NY, 1963 65(2016), 5, Seite 1015-1021 (DE-627)129358576 (DE-600)160442-9 (DE-576)014730863 0018-9456 nnns volume:65 year:2016 number:5 pages:1015-1021 http://dx.doi.org/10.1109/TIM.2016.2514782 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7390044 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_24 GBV_ILN_30 GBV_ILN_70 GBV_ILN_170 GBV_ILN_2014 GBV_ILN_2061 50.21 AVZ 53.00 AVZ AR 65 2016 5 1015-1021 |
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ddc 620 bkl 50.21 bkl 53.00 misc uncertainty evaluation misc Nonlinear operations misc Uncertainty misc random-fuzzy variables (RFVs) misc Measurement uncertainty misc systematic effects misc Monte Carlo methods misc Yttrium misc possibility distributions (PDs) misc Systematics misc Entropy |
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Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions |
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Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions |
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Ferrero, Alessandro |
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joint random-fuzzy variables: a tool for propagating uncertainty through nonlinear measurement functions |
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Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions |
| abstract |
A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide to the Expression of Uncertainty in Measurement is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of random-fuzzy variables. Under this approach, nonrandom contributions to uncertainty can also be considered. An experimental example is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach. |
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A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide to the Expression of Uncertainty in Measurement is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of random-fuzzy variables. Under this approach, nonrandom contributions to uncertainty can also be considered. An experimental example is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach. |
| abstract_unstemmed |
A still open issue, in uncertainty evaluation, is asymmetrical distributions of the values that can be attributed to the measurand. This problem generally becomes not negligible when the measurement function is highly nonlinear. In this case, the law of uncertainty propagation suggested by the Guide to the Expression of Uncertainty in Measurement is not correct any longer, and only Monte Carlo simulations can be used to obtain such distributions. This paper shows how this problem can be solved in a quite immediate way when measurement results are expressed in terms of random-fuzzy variables. Under this approach, nonrandom contributions to uncertainty can also be considered. An experimental example is reported and the results compared with those obtained by means of Monte Carlo simulations, showing the effectiveness of the proposed approach. |
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Joint Random-Fuzzy Variables: A Tool for Propagating Uncertainty Through Nonlinear Measurement Functions |
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