Radial basis function approximations: comparison and applications
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Radial basis function approximations: comparison and applications
Original language description
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 > 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
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-05534S" target="_blank" >GA17-05534S: Meshless methods for large scattered spatio-temporal vector data visualization</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Mathematical Modelling
ISSN
0307-904X
e-ISSN
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Volume of the periodical
51
Issue of the periodical within the volume
neuvedeno
Country of publishing house
US - UNITED STATES
Number of pages
16
Pages from-to
728-743
UT code for WoS article
000412253100041
EID of the result in the Scopus database
2-s2.0-85028988349