About: An artificial neural network approach to solve inverse reliability problems

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: An artificial neural network approach to solve inverse reliability problems Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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	P
http://linked.open...avai/riv/aktivity	P(7D08004), P(GA103/08/0752), P(IAA201720901)
http://linked.open...vai/riv/dodaniDat	2011
http://linked.open...aciTvurceVysledku	Lehký, David Novák, Drahomír
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoké učení technické v Brně / Fakulta stavební
http://linked.open...dnocenehoVysledku	246173
http://linked.open...ai/riv/idVysledku	RIV/00216305:26110/10:PU92077
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Artificial neural network, reliability problems, reliability analysissmall-sample simulation, Latin Hypercube Sampling (en)
http://linked.open.../riv/klicoveSlovo	Artificial neural network Latin Hypercube Sampling reliability analysissmall-sample simulation reliability problems
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	JM
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Risk based Performance Prediction and Lifetime Assessment of Concrete Structures Sensitivity and uncertainty analysis of reliability of building structures Soil-structure interaction stochastic modelling (SISMO)
http://linked.open...UplatneniVysledku	2010
http://linked.open...iv/tvurceVysledku	Lehký, David Novák, Drahomír
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2010-08-28 (xsd:date)
number of pages	7 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Neuveden
https://schema.org/isbn	978-7-5608-4388-9
http://localhost/t...ganizacniJednotka	26110
is http://linked.open...avai/riv/vysledek of	An artificial neural network approach to solve inverse reliability problems

Faceted Search & Find service v1.16.118 as of Jun 21 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 112 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software