Detail publikace

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

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.

Jazyk

en

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.

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.",
  annote="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.",
  booktitle="Proceedings of the International Conference on Civil, Urban and Environmental Engineering (CUEE2014)",
  chapter="112916",
  howpublished="online",
  year="2014",
  month="august",
  pages="161--169",
  type="conference paper"
}