Publication detail

Advanced Decomposition Techniques Applied to DOP

POPELA, P. SKLENÁŘ, J. MATOUŠEK, R. ROUPEC, J. MRÁZKOVÁ, E.

Original Title

Advanced Decomposition Techniques Applied to DOP

English Title

Advanced Decomposition Techniques Applied to DOP

Type

conference paper

Language

en

Original Abstract

In technical practice we are very often confronted with need to approximate functions from measured values. Another frequent task is a calculation of measure of central tendency of sample data. For a good reason the method of least squares and the statistics like mean or median are being used. The goal of this paper is to show some nonstandard metrics usable in tasks of creation of approximation model or in tasks of symbolic regression. These metrics, as will be shown, can be created using so-called generating function. It is important to note these metrics can affect robustness of created model concerning extremely deviated values. Using these exotic metrics in tasks of data approximation or symbolic regression we get nonlinear unconstrained optimization task. To solve such task it is necessary to use adequate optimization strategies such as soft-computing methods (evolution algorithms, HC12, differential evolution, etc.) or classical methods of nonlinear optimization (Nelder-Mead, conjugate gradient, Levenberg–Marquardt algorithm, etc.).

English abstract

In technical practice we are very often confronted with need to approximate functions from measured values. Another frequent task is a calculation of measure of central tendency of sample data. For a good reason the method of least squares and the statistics like mean or median are being used. The goal of this paper is to show some nonstandard metrics usable in tasks of creation of approximation model or in tasks of symbolic regression. These metrics, as will be shown, can be created using so-called generating function. It is important to note these metrics can affect robustness of created model concerning extremely deviated values. Using these exotic metrics in tasks of data approximation or symbolic regression we get nonlinear unconstrained optimization task. To solve such task it is necessary to use adequate optimization strategies such as soft-computing methods (evolution algorithms, HC12, differential evolution, etc.) or classical methods of nonlinear optimization (Nelder-Mead, conjugate gradient, Levenberg–Marquardt algorithm, etc.).

Keywords

metric, exotic metric, function approximation, generating function

RIV year

2012

Released

27.06.2012

Publisher

VUT

Location

Brno

ISBN

978-80-214-4540-6

Book

18th International Conference of Soft Computing, MENDEL 2012 (id 19255)

Edition

2012

Edition number

1

Pages from

582

Pages to

587

Pages count

6

BibTex


@inproceedings{BUT93361,
  author="Pavel {Popela} and Jaroslav {Sklenář} and Radomil {Matoušek} and Jan {Roupec} and Eva {Mrázková}",
  title="Advanced Decomposition Techniques Applied to DOP",
  annote="In technical practice we are very often confronted with need to approximate functions from measured
values. Another frequent task is a calculation of measure of central tendency of sample data. For a good reason
the method of least squares and the statistics like mean or median are being used. The goal of this paper is to show
some nonstandard metrics usable in tasks of creation of approximation model or in tasks of symbolic regression.
These metrics, as will be shown, can be created using so-called generating function. It is important to note these
metrics can affect robustness of created model concerning extremely deviated values. Using these exotic metrics
in tasks of data approximation or symbolic regression we get nonlinear unconstrained optimization task. To
solve such task it is necessary to use adequate optimization strategies such as soft-computing methods (evolution
algorithms, HC12, differential evolution, etc.) or classical methods of nonlinear optimization (Nelder-Mead,
conjugate gradient, Levenberg–Marquardt algorithm, etc.).",
  address="VUT",
  booktitle="18th International Conference of Soft Computing, MENDEL 2012 (id 19255)",
  chapter="93361",
  edition="2012",
  howpublished="print",
  institution="VUT",
  number="1",
  year="2012",
  month="june",
  pages="582--587",
  publisher="VUT",
  type="conference paper"
}