An artificial neural network approach to solve inverse reliability problems

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F10%3APU92077" target="_blank" >RIV/00216305:26110/10:PU92077 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
An artificial neural network approach to solve inverse reliability problems
Original language 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 (reliabilityindex 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
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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