Detail publikačního výsledku

Sensitivity analysis strategies for artificial neural networks modelling of engineering problems.

NOVÁK, D.; LEHKÝ, D.; PAN, L.; CAO, M.

Original Title

Sensitivity analysis strategies for artificial neural networks modelling of engineering problems.

English Title

Sensitivity analysis strategies for artificial neural networks modelling of engineering problems.

Type

Paper in proceedings (conference paper)

Original Abstract

The paper presents two different strategies for sensitivity analysis related to artificial neural networks: nonparametric rank-order statistical correlation and neural network committee-based sensitivity analysis. Numerical examples illustrate the usefulness and feasibility of both alternative approaches.

English abstract

The paper presents two different strategies for sensitivity analysis related to artificial neural networks: nonparametric rank-order statistical correlation and neural network committee-based sensitivity analysis. Numerical examples illustrate the usefulness and feasibility of both alternative approaches.

Keywords

Sensitivity analysis, neural networks, simulation, prediction model.

Key words in English

Sensitivity analysis, neural networks, simulation, prediction model.

Authors

NOVÁK, D.; LEHKÝ, D.; PAN, L.; CAO, M.

RIV year

2015

Released

19.08.2014

Location

Beijing

ISBN

978-1-78466-044-4

Book

Proceedings of the International Conference on Civil, Urban and Environmental Engineering (CUEE2014)

Pages from

161

Pages to

169

Pages count

9

Full text in the Digital Library

http://hdl.handle.net/

BibTex

@inproceedings{BUT112916,
  author="Drahomír {Novák} and David {Lehký} and L. {Pan} and Maosen {Cao}",
  title="Sensitivity analysis strategies for artificial neural networks modelling of engineering problems.",
  booktitle="Proceedings of the International Conference on Civil, Urban and Environmental Engineering (CUEE2014)",
  year="2014",
  pages="161--169",
  address="Beijing",
  isbn="978-1-78466-044-4"
}