Publication detail

Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches.

LEHKÝ, D. SLOWIK, O. NOVÁK, D.

Original Title

Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches.

Type

conference paper

Language

English

Original Abstract

The paper presents two alternative approaches to solve inverse reliability task – to determine the design parameters to achieve desired target reliabilities. The first approach is based on utilization of artificial neural networks and small-sample simulation Latin hypercube sampling. The second approach considers inverse reliability task as reliability-based optimization task using double-loop method and also small-sample simulation.

Keywords

Inverse Reliability, artificial neural network, reliability-based optimization, double-loop optimization, uncertainties, Latin hypercube sampling

Authors

LEHKÝ, D.; SLOWIK, O.; NOVÁK, D.

RIV year

2014

Released

19. 9. 2014

Location

Řecko

ISBN

978-3-662-44653-9

Book

Proceedings of the 10th IFIP WG 12.5 International Conference Artificial Intelligence Applications and Inovations (AIAI 2014)

Pages from

344

Pages to

353

Pages count

10

BibTex

@inproceedings{BUT112900,
  author="David {Lehký} and Ondřej {Slowik} and Drahomír {Novák}",
  title="Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches.",
  booktitle="Proceedings of the 10th IFIP WG 12.5 International Conference Artificial Intelligence Applications and Inovations (AIAI 2014)",
  year="2014",
  pages="344--353",
  address="Řecko",
  isbn="978-3-662-44653-9"
}