Pregled bibliografske jedinice broj: 763879
Automatic Design of Radial Basis Function Networks Through Enhanced Differential Evolution
Automatic Design of Radial Basis Function Networks Through Enhanced Differential Evolution // Proceedings of the 10th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2015, Lecture Notes in Computer Science, Vol. 9121 / Onieva, Enrique ; Santos, Igor ; Osaba, Eneko ; Quintián, Héctor ; Corchado, Emilio (ur.).
Bilbao, Španjolska: Springer, 2015. str. 244-256 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 763879 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Automatic Design of Radial Basis Function Networks Through Enhanced Differential Evolution
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 10th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2015, Lecture Notes in Computer Science, Vol. 9121
/ Onieva, Enrique ; Santos, Igor ; Osaba, Eneko ; Quintián, Héctor ; Corchado, Emilio - : Springer, 2015, 244-256
ISBN
978-3-319-19643-5
Skup
International Conference on Hybrid Artificial Intelligence Systems
Mjesto i datum
Bilbao, Španjolska, 22.06.2015. - 24.06.2015
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Differential evolution; Initial population; k-means; Neural network; Radial basis function
Sažetak
During the creation of a classification model, it is vital to keep track of numerous parameters and to produce a model based on the limited knowledge inferred often from very confined data. Methods which aid the construction or completely build the classification model automatically, present a fairly common research interest. This paper proposes an approach that employs differential evolution enhanced through the incorporation of additional knowledge concerning the problem in order to design a radial basis neural network. The knowledge is inferred from the unsupervised learning procedure which aims to ensure an initial population of good solutions. Also, the search space is dynamically adjusted i.e. narrowed during runtime in terms of the decision variables count. The results obtained on several datasets suggest that the proposed approach is able to find well performing networks while keeping the structure simple. Furthermore, a comparison with a differential evolution algorithm without the proposed enhancements and a particle swarm optimization algorithm was carried out illustrating the benefits of the proposed approach.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Projekti:
165-0362980-2002 - Postupci raspoređivanja u samoodrživim raspodijeljenim računalnim sustavima (Martinović, Goran, MZO ) ( CroRIS)
Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek
