Hybrid 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%3A06020041" target="_blank" >RIV/67985807:_____/02:06020041 - 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 Learning of RBF Networks.
Original language description
Three different learning methods for RBF networks and their combinations are presented. Standard gradient learning, three-step algorithm with unsupervised part, and evolutionary algorithm are introduced. Their performance is compared on two benchmark problems: Two spirals and Iris plants. The results show that three-step learning is usually the fastes, while gradient learning achieves better precision. The combination of these two approaches gives best results.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BA - General mathematics
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
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
Article name in the collection
Computational Science.
ISBN
3-540-43594-8
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
594-603
Publisher name
Springer
Place of publication
Berlin
Event location
Amsterdam [NL]
Event date
Apr 21, 2002
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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