000 03368nam a22005295i 4500
001 978-3-319-91026-0
003 DE-He213
005 20220801220705.0
007 cr nn 008mamaa
008 180503s2018 sz | s |||| 0|eng d
020 _a9783319910260
_9978-3-319-91026-0
024 7 _a10.1007/978-3-319-91026-0
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aPownuk, Andrew.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_951788
245 1 0 _aCombining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications
_h[electronic resource] /
_cby Andrew Pownuk, Vladik Kreinovich.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXI, 202 p. 2 illus., 1 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v773
505 0 _aIntroduction -- 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.
520 _aHow 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. .
650 0 _aComputational intelligence.
_97716
650 0 _aEngineering mathematics.
_93254
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aEngineering Mathematics.
_93254
700 1 _aKreinovich, Vladik.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_951789
710 2 _aSpringerLink (Online service)
_951790
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319910253
776 0 8 _iPrinted edition:
_z9783319910277
776 0 8 _iPrinted edition:
_z9783030081584
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v773
_951791
856 4 0 _uhttps://doi.org/10.1007/978-3-319-91026-0
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78836
_d78836