Conclusions

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

Abstract

In many practical application, we process measurement results and expert estimates. Measurements and expert estimates are never absolutely accurate, their results are slightly different from the actual (unknown) values of the corresponding quantities. It is therefore desirable to analyze how this measurement and estimation inaccuracy affects the results of data processing.

In many practical application, we process measurement results and expert estimates. Measurements and expert estimates are never absolutely accurate, their results are slightly different from the actual (unknown) values of the corresponding quantities. It is therefore desirable to analyze how this measurement and estimation inaccuracy affects the results of data processing.

There exist numerous methods for estimating the accuracy of the results of data processing under different models of measurement and estimation inaccuracies: probabilistic, interval, and fuzzy. To be useful in engineering applications, these methods:
  • should provide accurate estimate for the resulting uncertainty,

  • should not take too much computation time,

  • should be understandable to engineers, and

  • should be sufficiently general to cover all kinds of uncertainty.

In this book, on several case studies, we show how we can achieve these four objectives:
  • We show that we can get more accurate estimates by properly taking model inaccuracy into account.

  • We show that we can speed up computations by processing different types of uncertainty differently.

  • We show that we can make uncertainty-estimating algorithms more understandable.

  • We also analyze how general uncertainty-estimating algorithms can be.

We also analyze how to make decisions under different types of uncertainty.

We apply the corresponding algorithms to practical engineering problems, in particular, to the inverse problem in geosciences and to the problem of pavement compaction.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computational Science ProgramUniversity of Texas at El PasoEl PasoUSA

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