Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

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

David Lehký
  • Function : Author
  • PersonId : 992467
Ondřej Slowik
  • Function : Author
  • PersonId : 992468
Drahomír Novák
  • Function : Author
  • PersonId : 992469

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. Efficiency of both approaches is presented in numerical example, advantages and disadvantages are discussed.
Fichier principal
Vignette du fichier
978-3-662-44654-6_34_Chapter.pdf (395.98 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01391333 , version 1 (03-11-2016)

Licence

Attribution

Identifiers

Cite

David Lehký, Ondřej Slowik, Drahomír Novák. Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.344-353, ⟨10.1007/978-3-662-44654-6_34⟩. ⟨hal-01391333⟩
153 View
244 Download

Altmetric

Share

Gmail Facebook X LinkedIn More