Detail publikačního výsledku

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

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

Originální název

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

Anglický název

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

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

Sensitivity analysis, neural networks, simulation, prediction model.

Klíčová slova v angličtině

Sensitivity analysis, neural networks, simulation, prediction model.

Autoři

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

Rok RIV

2015

Vydáno

19.08.2014

Místo

Beijing

ISBN

978-1-78466-044-4

Kniha

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

Strany od

161

Strany do

169

Strany počet

9

Plný text v Digitální knihovně

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"
}