Comparing Fixed and Variable-Width Gaussian Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00428366" target="_blank" >RIV/67985807:_____/14:00428366 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.neunet.2014.05.005" target="_blank" >http://dx.doi.org/10.1016/j.neunet.2014.05.005</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neunet.2014.05.005" target="_blank" >10.1016/j.neunet.2014.05.005</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparing Fixed and Variable-Width Gaussian Networks

Popis výsledku v původním jazyce

The role of width of Gaussians in two types of computational models is investigated: Gaussian radial basis- functions (RBFs) where both widths and centers vary and Gaussian kernel networks which have fixed widths but varying centers. The effect of widthon functional equivalence, universal approximation property, and form of norms in reproducing kernel Hilbert spaces (RKHSs)is explored. It is proven that if two Gaussian RBF networks have the same input?output functions, then they must have the same numbers of units with the same centers and widths. Further, it is shown that while sets of input?output functions of Gaussian kernel networks with two different widths are disjoint, each such set is large enough to be a universal approximator. Embedding of RKHSs induced by flatter?? Gaussians into RKHSs induced by sharper?? Gaussians is described and growth of the ratios of norms on these spaces with increasing input dimension is estimated. Finally, large sets of argminima of error functiona

Název v anglickém jazyce

Comparing Fixed and Variable-Width Gaussian Networks

Popis výsledku anglicky

The role of width of Gaussians in two types of computational models is investigated: Gaussian radial basis- functions (RBFs) where both widths and centers vary and Gaussian kernel networks which have fixed widths but varying centers. The effect of widthon functional equivalence, universal approximation property, and form of norms in reproducing kernel Hilbert spaces (RKHSs)is explored. It is proven that if two Gaussian RBF networks have the same input?output functions, then they must have the same numbers of units with the same centers and widths. Further, it is shown that while sets of input?output functions of Gaussian kernel networks with two different widths are disjoint, each such set is large enough to be a universal approximator. Embedding of RKHSs induced by flatter?? Gaussians into RKHSs induced by sharper?? Gaussians is described and growth of the ratios of norms on these spaces with increasing input dimension is estimated. Finally, large sets of argminima of error functiona

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/LD13002" target="_blank" >LD13002: Modelování složitých systémů softcomputingovými metodami</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Neural Networks

ISSN

0893-6080

e-ISSN

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Svazek periodika

57

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

6

Strana od-do

23-28

Kód UT WoS článku

000340319400003

EID výsledku v databázi Scopus

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