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Effectiveness of differential evolution in training radial basis function networks for classification (CROSBI ID 642840)

Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija

Bajer, Dražen ; Zorić, Bruno ; Martinović, Goran ; Effectiveness of differential evolution in training radial basis function networks for classification // Proceedings of the 1st International Conference on Smart Systems and Technologies (SST) / Žagar, Drago ; Martinović, Goran ; Rimac Drlje, Snježana ; (ur.). Osijek: Fakultet elektrotehnike, računarstva i informacijskih tehnologija Sveučilišta Josipa Jurja Strossmayera u Osijeku, 2016. str. 179-184

Podaci o odgovornosti

Bajer, Dražen ; Zorić, Bruno ; Martinović, Goran ;

engleski

Effectiveness of differential evolution in training radial basis function networks for classification

Building classification models often presents a significant problem that requires the selection of a classifier and a corresponding training approach. Radial basis function networks are a frequent choice among the classifiers for which a large spectre of training approaches exist. In that regard, an important role is played by bio-inspired methods, and differential evolution, as an representative example, has been applied for training such networks. This paper investigates the behaviour of differential evolution in training radial basis function networks primarily from the perspective of fitting the model to available (training) data rather than its performance on unknown (testing) data. This is believed to provide a clearer insight into optimiser efficiency. Another important issue considered is a steady emergence of new bio-inspired methods claiming superior performance that can be witnessed in the literature. It may raise the question whether differential evolution is still competitive to those approaches. In light of this, the canonical differential evolution algorithm has been compared to a couple of recently proposed and a well established swarm intelligence algorithm.

Algorithm design and analysis; Optimization; Radial basis function networks; Silicon; Sociology; Statistics; Training; bio-inspired optimisation algorithms; classification; differential evolution; radial basis function networks

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Podaci o prilogu

179-184.

2016.

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objavljeno

978-1-5090-3718-6

Podaci o matičnoj publikaciji

Proceedings of the 1st International Conference on Smart Systems and Technologies (SST)

Žagar, Drago ; Martinović, Goran ; Rimac Drlje, Snježana ;

Osijek: Fakultet elektrotehnike, računarstva i informacijskih tehnologija Sveučilišta Josipa Jurja Strossmayera u Osijeku

Podaci o skupu

International Conference on Smart Systems and Technologies (SST)

predavanje

12.10.2016-14.10.2016

Osijek, Hrvatska

Povezanost rada

Računarstvo