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Learning Methods for Radial Basis Functions Networks

SYSNO ASEP	0405221
Document Type	J - Journal Article
R&D Document Type	Journal Article
Subsidiary J	Článek ve WOS
Title	Learning Methods for Radial Basis Functions Networks
Title	Metody učení pro neuronové sítě typu RBF
Author(s)	Neruda, Roman (UIVT-O)_{SAI, RID, ORCID} Kudová, Petra (UIVT-O)_{SAI, RID, ORCID}
Source Title	Future Generation Computer Systems. - : Elsevier - ISSN 0167-739X Roč. 21, - (2005), s. 1131-1142
Number of pages	12 s.
Language	eng - English
Country	NL - Netherlands
Keywords	radial basis function networks ; hybrid supervised learning ; genetic algorithms ; benchmarking
Subject RIV	BA - General Mathematics
R&D Projects	GP201/03/P163 GA ČR - Czech Science Foundation (CSF)
	GA201/02/0428 GA ČR - Czech Science Foundation (CSF)
CEZ	AV0Z10300504 - UIVT-O (2005-2011)
UT WOS	000230288700016
EID SCOPUS	27544458557
DOI	10.1016/j.future.2004.03.013
Annotation	In this paper we present and examine several learning methods for RBF networks and their combinations. Performance of individual methods and their combinations is compared on experiments. The best results can be achieved by employing hybrid approaches that combine presented methods.
Workplace	Institute of Computer Science
Contact	Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
Year of Publishing	2006

Number of the records: 1