Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications

1. Auflage 2018
ISBN: 978-3-319-91025-3
Verlag: Springer International Publishing

Buch, Englisch, Band 773, 202 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 494 g

Reihe: Studies in Computational Intelligence

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. 

Zielgruppe

Research

Autoren/Hrsg.

Kreinovich, Vladik

Pownuk, Andrew

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Introduction.- How to Get More Accurate Estimates.- How to Speed Up Computations.- Towards a Better Understandability of Uncertainty-Estimating Algorithms.- How General Can We Go: What Is Computable and What Is Not.- Decision Making Under Uncertainty.- Conclusions.