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Learning Methods for Radial Basis Functions Networks
- 1.0405221 - UIVT-O 330398 RIV NL eng J - Journal Article
Neruda, Roman - Kudová, Petra
Learning Methods for Radial Basis Functions Networks.
[Metody učení pro neuronové sítě typu RBF.]
Future Generation Computer Systems. Roč. 21, - (2005), s. 1131-1142. ISSN 0167-739X. E-ISSN 1872-7115
R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428
Institutional research plan: CEZ:AV0Z10300504
Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking
Subject RIV: BA - General Mathematics
Impact factor: 0.555, year: 2005
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.
V tomto článku popisujeme a studujeme několik algoritmů pro učení RBF sítí. Jednotlivé metody i jejich kombinace jsou porovnány na experimentech. Nejlepších výsledku lze dosáhnout pomocí hybridního přístupu, který kombinuje několik metod.
Permanent Link: http://hdl.handle.net/11104/0125412
Number of the records: 1