Detail publikace

The Algorithm for Choice of Samples in Training Set for Neural Networks

Originální název

The Algorithm for Choice of Samples in Training Set for Neural Networks

Anglický název

The Algorithm for Choice of Samples in Training Set for Neural Networks

Jazyk

en

Originální abstrakt

The paper deals with on-line system identification for adaptive controller construction. A regressive model is used for system modelling. The regressive function is realized in neural network. The problem of an increasing number of data for neural network learning is solved by the choice of patterns in the training set based on the distance between patterns in the space of regressive model state vectors. Furthermore, the choice of scale to set this distance is considered. The objective is minimization of empirical approaches to allow for automation and wide application.

Anglický abstrakt

The paper deals with on-line system identification for adaptive controller construction. A regressive model is used for system modelling. The regressive function is realized in neural network. The problem of an increasing number of data for neural network learning is solved by the choice of patterns in the training set based on the distance between patterns in the space of regressive model state vectors. Furthermore, the choice of scale to set this distance is considered. The objective is minimization of empirical approaches to allow for automation and wide application.

BibTex


@inproceedings{BUT4917,
  author="Hynek {Vychodil} and Petr {Pivoňka} and Petr {Krupanský}",
  title="The Algorithm for Choice of Samples in Training Set for Neural Networks",
  annote="The paper deals with on-line system identification for adaptive controller construction. A regressive model is used for system modelling. The regressive function is realized in neural network. The problem of an increasing number of data for neural network learning is solved by the choice of patterns in the training set based on the distance between patterns in the space of regressive model state vectors. Furthermore, the choice of scale to set this distance is considered. The objective is minimization of empirical approaches to allow for automation and wide application.",
  address="Rektor der Hochschule Zittau/Görlitz",
  booktitle="Proceedings East West Fuzzy Colloquium 2002",
  chapter="4917",
  institution="Rektor der Hochschule Zittau/Görlitz",
  year="2002",
  month="september",
  pages="224",
  publisher="Rektor der Hochschule Zittau/Görlitz",
  type="conference paper"
}