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

  • Andrew Pownuk
  • Vladik Kreinovich

Part of the Studies in Computational Intelligence book series (SCI, volume 773)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Andrew Pownuk, Vladik Kreinovich
    Pages 1-12
  3. Andrew Pownuk, Vladik Kreinovich
    Pages 13-44
  4. Andrew Pownuk, Vladik Kreinovich
    Pages 45-95
  5. Andrew Pownuk, Vladik Kreinovich
    Pages 97-136
  6. Andrew Pownuk, Vladik Kreinovich
    Pages 137-155
  7. Andrew Pownuk, Vladik Kreinovich
    Pages 157-190
  8. Andrew Pownuk, Vladik Kreinovich
    Pages 191-191
  9. Back Matter
    Pages 193-202

About this book


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. 


Computational Intelligence Interval Uncertainty Uncertainty Probabilistic Uncertainty Fuzziness

Authors and affiliations

  • Andrew Pownuk
    • 1
  • Vladik Kreinovich
    • 2
  1. 1.Computational Science ProgramUniversity of Texas at El PasoEl PasoUSA
  2. 2.Computational Science ProgramUniversity of Texas at El PasoEl PasoUSA

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-91025-3
  • Online ISBN 978-3-319-91026-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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