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5 August 2009 Application of differential evolution algorithm for automatic constructing and adapting radial basis function neural networks
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Proceedings Volume 7502, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009; 75022G (2009) https://doi.org/10.1117/12.839615
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009, 2009, Wilga, Poland
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.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dawid Rymszo and Stanislaw Jankowski "Application of differential evolution algorithm for automatic constructing and adapting radial basis function neural networks", Proc. SPIE 7502, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2009, 75022G (5 August 2009); https://doi.org/10.1117/12.839615
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