Authors:
- Presents successful methods for estimating the accuracy of the results of data processing under different models of measurement and estimation inaccuracies: probabilistic, interval, and fuzzy
- Offers methods that provide accurate estimates of the resulting uncertainty, do not take too much computation time, will be accessible for engineers, and are sufficiently general to cover all kinds of uncertainty
- Includes several illustrative case studies
Part of the book series: Studies in Computational Intelligence (SCI, volume 773)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
About this book
In all these developments, the authors’ objectives were to provide accurate estimates of the resulting uncertainty; to offer solutions that require reasonably short computation times; to offer content that is accessible for engineers; and to be sufficiently general - so that readers can use the book for many different problems. The authors also describe how to make decisions under different types of uncertainty.
The book offers a valuable resource for all practical engineers interested in better ways of gauging uncertainty, for students eager to learn and apply the new techniques, and for researchers interested in processing heterogeneous uncertainty.
Reviews
“The book is well structured and easy to work through. Without confusing detours, the authors always come directly to the point, clearly explaining what they are doing and why.” (Heinrich Hering, zbMATH 1432.93003, 2020)
Authors and Affiliations
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Computational Science Program, University of Texas at El Paso, El Paso, USA
Andrew Pownuk, Vladik Kreinovich
Bibliographic Information
Book Title: Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications
Authors: Andrew Pownuk, Vladik Kreinovich
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-91026-0
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-91025-3Published: 18 May 2018
Softcover ISBN: 978-3-030-08158-4Published: 20 December 2018
eBook ISBN: 978-3-319-91026-0Published: 03 May 2018
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XI, 202
Number of Illustrations: 1 b/w illustrations, 1 illustrations in colour