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Pregled bibliografske jedinice broj: 849446

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


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: Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2016. str. 179-184 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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

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

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 1st International Conference on Smart Systems and Technologies (SST) / Žagar, Drago ; Martinović, Goran ; Rimac Drlje, Snježana ; - Osijek : Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2016, 179-184

ISBN
978-1-5090-3718-6

Skup
International Conference on Smart Systems and Technologies (SST)

Mjesto i datum
Osijek, Hrvatska, 12-14.10.2016

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek