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

• Main Authors: Pownuk, Andrew (Author, http://id.loc.gov/vocabulary/relators/aut), Kreinovich, Vladik (http://id.loc.gov/vocabulary/relators/aut)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: Studies in Computational Intelligence, 773
• Subjects: Computational intelligence.; Engineering mathematics.; Computational Intelligence.; Engineering Mathematics.

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 thes...

Table of Contents:

• 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.