The ICP for Fragment Identification

článek ve sborníku ve WoS nebo Scopus

angličtina

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.).

metric, exotic metric, function approximation, generating function

JANČÍK, S.; MATOUŠEK, R.; DVOŘÁK, J.; ABBADI, A.

2012

27. 6. 2012

VUT

Brno

978-80-214-4540-6

18th International Conference of Soft Computing, MENDEL 2012

2011

1

1803-3814

Mendel Journal series

2012

1

Česká republika

588

593

6