Detail publikace

Artificial neural network based inverse reliability analysis

Originální název

Artificial neural network based inverse reliability analysis

Anglický název

Artificial neural network based inverse reliability analysis

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

BibTex


@article{BUT50904,
  author="David {Lehký} and Drahomír {Novák}",
  title="Artificial neural network based inverse reliability analysis",
  annote="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.",
  chapter="50904",
  journal="Proceedings in Applied Mathematics and Mechanics",
  number="10",
  volume="1",
  year="2010",
  month="october",
  pages="187--188",
  type="journal article - other"
}