Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

BibTex


@inproceedings{BUT112900,
  author="David {Lehký} and Ondřej {Slowik} and Drahomír {Novák}",
  title="Inverse Reliability Task: Artificial Neural Networks and Reliability-Based Optimization Approaches.",
  annote="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.",
  booktitle="Proceedings of the 10th IFIP WG 12.5 International Conference Artificial Intelligence Applications and Inovations (AIAI 2014)",
  chapter="112900",
  howpublished="print",
  year="2014",
  month="september",
  pages="344--353",
  type="conference paper"
}