東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Measuring uncertainty within the the...

Salicone, Simona.

FindBook

Google Book

Amazon

博客來

Measuring uncertainty within the theory of evidence

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Measuring uncertainty within the theory of evidence/ by Simona Salicone, Marco Prioli.
作者:	Salicone, Simona.
其他作者:	Prioli, Marco.
出版者:	Cham :Springer International Publishing : : 2018.,
面頁冊數:	xv, 330 p. :ill., digital ;24 cm.
內容註:	1. Introduction -- Part I: The background of the Measurement Uncertainty -- 2. Measurements -- 3. Mathematical Methods to handle Measurement Uncertainty -- 4. A first, preliminary example -- Part II: The mathematical Theory of the Evidence -- 5. Introduction: probability and belief functions -- 6. Basic definitions of the Theory of Evidence -- 7. Particular cases of the Theory of Evidence -- 8. Operators between possibility distributions -- 9. The joint possibility distributions -- 10. The combination of the possibility distributions -- 11. The comparison of the possibility distributions -- 12. The Probability-Possibility Transformations -- Part III: The Fuzzy Set Theory and the Theory of the Evidence -- 13. A short review of the Fuzzy Set Theory -- 14. The relationship between the Fuzzy Set Theory and the Theory of Evidence -- Part IV: Measurement Uncertainty within the mathematical framework of the Theory of the Evidence -- 15. Introduction: towards an alternative representation of the Measurement Results -- 16. Random-Fuzzy Variables and Measurement Results -- 17. The Joint Random-Fuzzy variables -- 18. The Combination of the Random-Fuzzy Variables -- 19. The Comparison of the Random-Fuzzy Variables -- 20. Measurement Uncertainty within Fuzzy Inference Systems -- Part V: Application examples -- 21. Phantom Power measurement -- 22. Characterization of a resistive voltage divider -- 23. Temperature measurement update -- 24. The Inverted Pendulum -- 25. Conclusion -- References -- Index.
Contained By:	Springer eBooks
標題:	Measurement uncertainty (Statistics) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-74139-0
ISBN:	9783319741390

Measuring uncertainty within the theory of evidence
Salicone, Simona.

Measuring uncertainty within the theory of evidence[electronic resource] /by Simona Salicone, Marco Prioli. - Cham :Springer International Publishing :2018. - xv, 330 p. :ill., digital ;24 cm. - Springer series in measurement science and technology,2198-7807. - Springer series in measurement science and technology..

1. Introduction -- Part I: The background of the Measurement Uncertainty -- 2. Measurements -- 3. Mathematical Methods to handle Measurement Uncertainty -- 4. A first, preliminary example -- Part II: The mathematical Theory of the Evidence -- 5. Introduction: probability and belief functions -- 6. Basic definitions of the Theory of Evidence -- 7. Particular cases of the Theory of Evidence -- 8. Operators between possibility distributions -- 9. The joint possibility distributions -- 10. The combination of the possibility distributions -- 11. The comparison of the possibility distributions -- 12. The Probability-Possibility Transformations -- Part III: The Fuzzy Set Theory and the Theory of the Evidence -- 13. A short review of the Fuzzy Set Theory -- 14. The relationship between the Fuzzy Set Theory and the Theory of Evidence -- Part IV: Measurement Uncertainty within the mathematical framework of the Theory of the Evidence -- 15. Introduction: towards an alternative representation of the Measurement Results -- 16. Random-Fuzzy Variables and Measurement Results -- 17. The Joint Random-Fuzzy variables -- 18. The Combination of the Random-Fuzzy Variables -- 19. The Comparison of the Random-Fuzzy Variables -- 20. Measurement Uncertainty within Fuzzy Inference Systems -- Part V: Application examples -- 21. Phantom Power measurement -- 22. Characterization of a resistive voltage divider -- 23. Temperature measurement update -- 24. The Inverted Pendulum -- 25. Conclusion -- References -- Index.

This monograph considers the evaluation and expression of measurement uncertainty within the mathematical framework of the Theory of Evidence. With a new perspective on the metrology science, the text paves the way for innovative applications in a wide range of areas. Building on Simona Salicone's Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence, the material covers further developments of the Random Fuzzy Variable (RFV) approach to uncertainty and provides a more robust mathematical and metrological background to the combination of measurement results that leads to a more effective RFV combination method. While the first part of the book introduces measurement uncertainty, the Theory of Evidence, and fuzzy sets, the following parts bring together these concepts and derive an effective methodology for the evaluation and expression of measurement uncertainty. A supplementary downloadable program allows the readers to interact with the proposed approach by generating and combining RFVs through custom measurement functions. With numerous examples of applications, this book provides a comprehensive treatment of the RFV approach to uncertainty that is suitable for any graduate student or researcher with interests in the measurement field.

ISBN: 9783319741390

Standard No.: 10.1007/978-3-319-74139-0doiSubjects--Topical Terms:

2010713
Measurement uncertainty (Statistics)

LC Class. No.: QA276.8

Dewey Class. No.: 003.54

Measuring uncertainty within the theory of evidence
LDR:03850nmm a2200337 a 4500 001 2142251
003 DE-He213
005 20180423190745.0
006 m d
007 cr nn 008maaau
008 181214s2018 gw s 0 eng d
020 $a 9783319741390 $q (electronic bk.)
020 $a 9783319741376 $q (paper)
024 7 $a 10.1007/978-3-319-74139-0 $2 doi
035 $a 978-3-319-74139-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276.8
072 7 $a PBT $2 bicssc
072 7 $a PBWL $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 003.54 $2 23
090 $a QA276.8 $b .S165 2018
100 1 $a Salicone, Simona. $3 3321722
245 1 0 $a Measuring uncertainty within the theory of evidence $h [electronic resource] / $c by Simona Salicone, Marco Prioli.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xv, 330 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in measurement science and technology, $x 2198-7807
505 0 $a 1. Introduction -- Part I: The background of the Measurement Uncertainty -- 2. Measurements -- 3. Mathematical Methods to handle Measurement Uncertainty -- 4. A first, preliminary example -- Part II: The mathematical Theory of the Evidence -- 5. Introduction: probability and belief functions -- 6. Basic definitions of the Theory of Evidence -- 7. Particular cases of the Theory of Evidence -- 8. Operators between possibility distributions -- 9. The joint possibility distributions -- 10. The combination of the possibility distributions -- 11. The comparison of the possibility distributions -- 12. The Probability-Possibility Transformations -- Part III: The Fuzzy Set Theory and the Theory of the Evidence -- 13. A short review of the Fuzzy Set Theory -- 14. The relationship between the Fuzzy Set Theory and the Theory of Evidence -- Part IV: Measurement Uncertainty within the mathematical framework of the Theory of the Evidence -- 15. Introduction: towards an alternative representation of the Measurement Results -- 16. Random-Fuzzy Variables and Measurement Results -- 17. The Joint Random-Fuzzy variables -- 18. The Combination of the Random-Fuzzy Variables -- 19. The Comparison of the Random-Fuzzy Variables -- 20. Measurement Uncertainty within Fuzzy Inference Systems -- Part V: Application examples -- 21. Phantom Power measurement -- 22. Characterization of a resistive voltage divider -- 23. Temperature measurement update -- 24. The Inverted Pendulum -- 25. Conclusion -- References -- Index.
520 $a This monograph considers the evaluation and expression of measurement uncertainty within the mathematical framework of the Theory of Evidence. With a new perspective on the metrology science, the text paves the way for innovative applications in a wide range of areas. Building on Simona Salicone's Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence, the material covers further developments of the Random Fuzzy Variable (RFV) approach to uncertainty and provides a more robust mathematical and metrological background to the combination of measurement results that leads to a more effective RFV combination method. While the first part of the book introduces measurement uncertainty, the Theory of Evidence, and fuzzy sets, the following parts bring together these concepts and derive an effective methodology for the evaluation and expression of measurement uncertainty. A supplementary downloadable program allows the readers to interact with the proposed approach by generating and combining RFVs through custom measurement functions. With numerous examples of applications, this book provides a comprehensive treatment of the RFV approach to uncertainty that is suitable for any graduate student or researcher with interests in the measurement field.
650 0 $a Measurement uncertainty (Statistics) $3 2010713
650 0 $a Uncertainty (Information theory) $3 587701
650 1 4 $a Mathematics. $3 515831
650 2 4 $a Probability Theory and Stochastic Processes. $3 891080
700 1 $a Prioli, Marco. $3 3321723
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Springer series in measurement science and technology. $3 2068767
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-74139-0
950 $a Mathematics and Statistics (Springer-11649)