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  • In present we benefit from the use of nature processes which provide us with highly effective heuristics for solving various problems. Their advantages are mainly prominent in hybrid approach. This paper evaluates several approaches for learning neural network based on Radial Basis Function (RBF) for distinguishing different sets in R^L. RBF networks use one layer of hidden RBF units and the number of RBF units is kept constatnt. In the paper we evaluate the ACO_R (Ant Colony Approach for Real domain) approach inspired by ant behavior and the PSO (Particle Swarm Optimization) algorithm inspired by behavior of flock of birds or fish in the nature. Nature inspired and classical algorithms are compared and evaluated.
  • In present we benefit from the use of nature processes which provide us with highly effective heuristics for solving various problems. Their advantages are mainly prominent in hybrid approach. This paper evaluates several approaches for learning neural network based on Radial Basis Function (RBF) for distinguishing different sets in R^L. RBF networks use one layer of hidden RBF units and the number of RBF units is kept constatnt. In the paper we evaluate the ACO_R (Ant Colony Approach for Real domain) approach inspired by ant behavior and the PSO (Particle Swarm Optimization) algorithm inspired by behavior of flock of birds or fish in the nature. Nature inspired and classical algorithms are compared and evaluated. (en)
  • Přírodní procesy nabízí vysoce efektivní heuristické přístupy, které lze využít pro řešení různých problémů. Mezi nejznámější příklady patří neuronové sítě. Tento příspěvek hodnotí různé přístupy pro učení RBF neuronových sítí - optimalizace inspirovaná mravenčími procesy pro reálné problémy (ACO_R) a optimalizace hejnem částic (PSO). Článek uvádí základní popis a porovnání příslušných metod a základní metody učení. (cs)
Title
  • Nature Inspired Methods in the Radial Basis Function Network Learning Process
  • Nature Inspired Methods in the Radial Basis Function Network Learning Process (en)
  • Využití metod inspirovaných přírodou v procesu učení sítí RBF (cs)
skos:prefLabel
  • Nature Inspired Methods in the Radial Basis Function Network Learning Process
  • Nature Inspired Methods in the Radial Basis Function Network Learning Process (en)
  • Využití metod inspirovaných přírodou v procesu učení sítí RBF (cs)
skos:notation
  • RIV/68407700:21230/08:03151584!RIV09-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 381813
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/08:03151584
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • RBF; RBF neural network learning; ant colony optimization; particle swarm optimalization (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [E55F7C5D49BA]
http://linked.open...v/mistoKonaniAkce
  • Prague
http://linked.open...i/riv/mistoVydani
  • Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Artificial Neural Networks - ICANN 2008
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Burša, Miroslav
  • Lhotská, Lenka
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000259567200086
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
issn
  • 0302-9743
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-540-87558-1
http://localhost/t...ganizacniJednotka
  • 21230
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