Hybrid of Learning of RBF Networks.
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F02%3A06020228" target="_blank" >RIV/67985807:_____/02:06020228 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Hybrid of Learning of RBF Networks.
Original language description
Three different learning methods for RBF networks and their combinations are presented. Their performance is compared on two benchmarks problems: Two spirals and Iris plants. The results show that the three-step learning is usually the fastest, while thegradient learning achieves better precision.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BA - General mathematics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA201%2F00%2F1489" target="_blank" >GA201/00/1489: Soft Computing: Theoretical foundations and experiments</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2002
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Network World
ISSN
1210-0552
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
6
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
13
Pages from-to
573-585
UT code for WoS article
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EID of the result in the Scopus database
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