Detail publikace

Advanced Decomposition Techniques Applied to DOP

Originální název

Advanced Decomposition Techniques Applied to DOP

Anglický název

Advanced Decomposition Techniques Applied to DOP

Jazyk

en

Originální abstrakt

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.).

Anglický abstrakt

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.).

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"
}