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Proceedings Paper

Application of differential evolution algorithm for automatic constructing and adapting radial basis function neural networks
Author(s): Dawid Rymszo; Stanislaw Jankowski
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Paper Abstract

The paper presents a new approach to automatic constructing and training Radial Basis Function (RBF) neural networks based on Differential Evolution (DE) algorithm. The method, called Differential Evolution-Radial Basis Function Network (DE-RBFN) is tested on approximation tasks of exemplary one- and two- dimensional Gaussian functions. Experiments are performed in Matlab environment. The results show that application of DE-RBFN enables to obtain optimal sparse network architecture by tuning the position and width of each basis function. The performance of the method is better than other related procedures applied to RBF networks.

Paper Details

Date Published: 5 August 2009
PDF: 8 pages
Proc. SPIE 7502, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009, 75022G (5 August 2009); doi: 10.1117/12.839615
Show Author Affiliations
Dawid Rymszo, Warsaw Univ. of Technology (Poland)
Stanislaw Jankowski, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 7502:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009
Ryszard S. Romaniuk; Krzysztof S. Kulpa, Editor(s)

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