crta
Hrvatska znanstvena Sekcija img
bibliografija
3 gif
 Naslovna
 O projektu
 FAQ
 Kontakt
4 gif
Pregledavanje radova
Jednostavno pretraživanje
Napredno pretraživanje
Skupni podaci
Upis novih radova
Upute
Ispravci prijavljenih radova
Ostale bibliografije
Slični projekti
 Bibliografske baze podataka

Pregled bibliografske jedinice broj: 849446

Zbornik radova

Autori: Bajer, Dražen; Zorić, Bruno; Martinović, Goran;
Naslov: Effectiveness of differential evolution in training radial basis function networks for classification
Izvornik: 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. 179-184 (ISBN: 978-1-5090-3718-6).
Dio CC časopisa: NE
Skup: International Conference on Smart Systems and Technologies (SST)
Mjesto i datum: Osijek, Hrvatska, 12-14.10.2016.
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.
Vrsta sudjelovanja: Predavanje
Vrsta prezentacije u zborniku: Cjeloviti rad (in extenso)
Vrsta recenzije: Međunarodna recenzija
Izvorni jezik: ENG
Kategorija: Znanstveni
Znanstvena područja:
Računarstvo
Tiskani medij: da
CD/DVD medij: da
URL Internet adrese: http://ieeexplore.ieee.org/document/7765655/
Upisao u CROSBI: Bruno Zorić (bruno.zoric@etfos.hr), 13. Pro. 2016. u 14:45 sati



Verzija za printanje   za tiskati


upomoc
foot_4