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商品詳細
Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications. Softcover Reprint of the Original 1st 2018 ed.
・ISBN 978-3-030-08158-4 paper EUR 99.99
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お気に入り
★★★
| 著者・編者 | Pownuk, Andrew / Kreinovich, Vladik, |
|---|---|
| シリーズ | (Studies in Computational Intelligence) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2018 |
| ページ数 | 202 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A03-94303＞ |
解説
How can we solve engineering problems while taking into account data characterized by different types of measurement and estimation uncertainty: interval, probabilistic, fuzzy, etc.? This book provides a theoretical basis for arriving at such solutions, as well as case studies demonstrating how these theoretical ideas can be translated into practical applications in the geosciences, pavement engineering, etc.
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.
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.