书目名称 | Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications | 编辑 | Andrew Pownuk,Vladik Kreinovich | 视频video | http://file.papertrans.cn/231/230091/230091.mp4 | 概述 | 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 m | 丛书名称 | Studies in Computational Intelligence | 图书封面 |  | 描述 | 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. . | 出版日期 | Book 2018 | 关键词 | Computational Intelligence; Interval Uncertainty; Uncertainty; Probabilistic Uncertainty; Fuzziness | 版次 | 1 | doi | https://doi.org/10.1007/978-3-319-91026-0 | isbn_softcover | 978-3-030-08158-4 | isbn_ebook | 978-3-319-91026-0Series ISSN 1860-949X Series E-ISSN 1860-9503 | issn_series | 1860-949X | copyright | Springer International Publishing AG, part of Springer Nature 2018 |
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