Radial basis function approximations: comparison and applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932144" target="_blank" >RIV/49777513:23520/17:43932144 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.apm.2017.07.033" target="_blank" >http://dx.doi.org/10.1016/j.apm.2017.07.033</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.apm.2017.07.033" target="_blank" >10.1016/j.apm.2017.07.033</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Radial basis function approximations: comparison and applications

Popis výsledku v původním jazyce

Approximation of scattered data is often a task in many engineering problems. The radial basis function (RBF) approximation is appropriate for large scattered (unordered) datasets in d-dimensional space. This approach is useful for a higher dimension d &gt; 2, because the other methods require the conversion of a scattered dataset to an ordered dataset (i.e. a semi-regular mesh is obtained by using some tessellation techniques), which is computationally expensive. The RBF approximation is non-separable, as it is based on the distance between two points. This method leads to a solution of linear system of equations (LSE) Ac=h. In this paper several RBF approximation methods are briefly introduced and a comparison of those is made with respect to the stability and accuracy of computation. The proposed RBF approximation offers lower memory requirements and better quality of approximation

Název v anglickém jazyce

Radial basis function approximations: comparison and applications

Popis výsledku anglicky

Approximation of scattered data is often a task in many engineering problems. The radial basis function (RBF) approximation is appropriate for large scattered (unordered) datasets in d-dimensional space. This approach is useful for a higher dimension d &gt; 2, because the other methods require the conversion of a scattered dataset to an ordered dataset (i.e. a semi-regular mesh is obtained by using some tessellation techniques), which is computationally expensive. The RBF approximation is non-separable, as it is based on the distance between two points. This method leads to a solution of linear system of equations (LSE) Ac=h. In this paper several RBF approximation methods are briefly introduced and a comparison of those is made with respect to the stability and accuracy of computation. The proposed RBF approximation offers lower memory requirements and better quality of approximation

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-05534S" target="_blank" >GA17-05534S: Meshless metody pro vizualizaci velkých časově-prostorových vektorových dat</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Mathematical Modelling

ISSN

0307-904X

e-ISSN

—

Svazek periodika

51

Číslo periodika v rámci svazku

neuvedeno

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

16

Strana od-do

728-743

Kód UT WoS článku

000412253100041

EID výsledku v databázi Scopus

2-s2.0-85028988349