Automatic Design of Radial Basis Function Networks Through Enhanced Differential Evolution (CROSBI ID 624632)
Prilog sa skupa u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Bajer, Dražen ; Zorić, Bruno ; Martinović, Goran
engleski
Automatic Design of Radial Basis Function Networks Through Enhanced Differential Evolution
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.
Differential evolution; Initial population; k-means; Neural network; Radial basis function
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Podaci o prilogu
244-256.
2015.
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objavljeno
Podaci o matičnoj publikaciji
Lecture notes in computer science
Onieva, Enrique ; Santos, Igor ; Osaba, Eneko ; Quintián, Héctor ; Corchado, Emilio
Springer
978-3-319-19643-5
0302-9743
Podaci o skupu
International Conference on Hybrid Artificial Intelligence Systems
predavanje
22.06.2015-24.06.2015
Bilbao, Španjolska