Publication detail

ANN inverse analysis based on stochastic small-sample training set simulation

NOVÁK, D. LEHKÝ, D.

Original Title

ANN inverse analysis based on stochastic small-sample training set simulation

Type

journal article - other

Language

English

Original Abstract

A new approach of inverse analysis is proposed to obtain parameters of a computational model in order to achieve the best agreement with experimental data. The inverse analysis is based on the coupling of a stochastic simulation and an artificial neural network (ANN). The identification parameters play the role of basic random variables with a scater reflecting the physical range of potential values. A nonovelty of the approach is the utilization of the efficient small-sample simulation method LatinHypercube Sampling (LHS) used for the stochastic preparation of the training set utilized in training the artificial neural network.

Keywords

Inverse analysis, identification, Latin hypercube sampling, artifical neural network, concrete

Authors

NOVÁK, D.; LEHKÝ, D.

RIV year

2006

Released

19. 5. 2006

Publisher

Elsevier

Location

Velká Británie

ISBN

0952-1976

Periodical

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Year of study

19

Number

5

State

United Kingdom of Great Britain and Northern Ireland

Pages from

731

Pages to

740

Pages count

10

BibTex

@article{BUT44443,
  author="Drahomír {Novák} and David {Lehký}",
  title="ANN inverse analysis based on stochastic small-sample training set simulation",
  journal="ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE",
  year="2006",
  volume="19",
  number="5",
  pages="731--740",
  issn="0952-1976"
}