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  • An artificial neural network approach to solve inverse reliability problems is proposed. An inverse reliability analysis is the problem to find design parameters corresponding to specified reliability levels expressed by reliability measures (reliability index or 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. That is needed for proper adjustment of synaptic weights and biases of s
  • An artificial neural network approach to solve inverse reliability problems is proposed. An inverse reliability analysis is the problem to find design parameters corresponding to specified reliability levels expressed by reliability measures (reliability index or 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. That is needed for proper adjustment of synaptic weights and biases of s (en)
Title
  • An artificial neural network approach to solve inverse reliability problems
  • An artificial neural network approach to solve inverse reliability problems (en)
skos:prefLabel
  • An artificial neural network approach to solve inverse reliability problems
  • An artificial neural network approach to solve inverse reliability problems (en)
skos:notation
  • RIV/00216305:26110/10:PU92077!RIV11-GA0-26110___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(7D08004), P(GA103/08/0752), P(IAA201720901)
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
  • 246173
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26110/10:PU92077
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Artificial neural network, reliability problems, reliability analysissmall-sample simulation, Latin Hypercube Sampling (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [29428F431DA4]
http://linked.open...v/mistoKonaniAkce
  • Shanghai
http://linked.open...i/riv/mistoVydani
  • Shanghai, Čína
http://linked.open...i/riv/nazevZdroje
  • Reliability Engineering and Risk Management
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...iv/tvurceVysledku
  • Lehký, David
  • Novák, Drahomír
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Neuveden
https://schema.org/isbn
  • 978-7-5608-4388-9
http://localhost/t...ganizacniJednotka
  • 26110
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