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Description
  • An inverse reliability analysis is the problem to find design parameters corresponding to specified reliability levels expressed by reliability index or by theoretical failure probability. Design parameters can be deterministic or they can be associated to random variables described by statistical moments. The aim is to solve generally not only the single design parameter case but also the multiple parameter problems with given multiple reliability constraints. A new general approach of inverse reliability analysis is proposed. The inverse analysis is based on the coupling of a stochastic simulation of Monte Carlo type and an artificial neural network. A novelty of the approach is the utilization of the efficient small-sample simulation method Latin Hypercube Sampling used for the stochastic preparation of the training set.
  • An inverse reliability analysis is the problem to find design parameters corresponding to specified reliability levels expressed by reliability index or by theoretical failure probability. Design parameters can be deterministic or they can be associated to random variables described by statistical moments. The aim is to solve generally not only the single design parameter case but also the multiple parameter problems with given multiple reliability constraints. A new general approach of inverse reliability analysis is proposed. The inverse analysis is based on the coupling of a stochastic simulation of Monte Carlo type and an artificial neural network. A novelty of the approach is the utilization of the efficient small-sample simulation method Latin Hypercube Sampling used for the stochastic preparation of the training set. (en)
Title
  • Artificial neural network based inverse reliability analysis
  • Artificial neural network based inverse reliability analysis (en)
skos:prefLabel
  • Artificial neural network based inverse reliability analysis
  • Artificial neural network based inverse reliability analysis (en)
skos:notation
  • RIV/00216305:26110/10:PU90943!RIV11-MSM-26110___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(7D08004), S
http://linked.open...iv/cisloPeriodika
  • 10
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 247790
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26110/10:PU90943
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http://linked.open.../riv/klicovaSlova
  • Neural network, reliability analysis (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [13AD75103364]
http://linked.open...i/riv/nazevZdroje
  • Proceedings in Applied Mathematics and Mechanics
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 1
http://linked.open...iv/tvurceVysledku
  • Lehký, David
  • Novák, Drahomír
issn
  • 1617-7061
number of pages
http://localhost/t...ganizacniJednotka
  • 26110
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