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Generalised Gaussian radial basis function neural networks

Authors :
César Hervás-Martínez
Francisco Fernández-Navarro
Pedro Antonio Gutiérrez
Source :
Soft Computing. 17:519-533
Publication Year :
2012
Publisher :
Springer Science and Business Media LLC, 2012.

Abstract

The mixed use of different shapes of radial basis functions (RBFs) in radial basis functions neural networks (RBFNNs) is investigated in this paper. For this purpose, we propose the use of a generalised version of the standard RBFNN, based on the generalised Gaussian distribution. The generalised radial basis function (GRBF) proposed in this paper is able to reproduce other different radial basis functions (RBFs) by changing a real parameter ?. In the proposed methodology, a hybrid evolutionary algorithm (HEA) is employed to estimate the number of hidden neuron, the centres, type and width of each RBF associated with each radial unit. In order to test the performance of the proposed methodology, an experimental study is presented with 20 datasets from the UCI repository. The GRBF neural network (GRBFNN) was compared to RBFNNs with Gaussian, Cauchy and inverse multiquadratic RBFs in the hidden layer and to other classifiers, including different RBFNN design methods, support vector machines (SVMs), a sparse probabilistic classifier (sparse multinominal logistic regression, SMLR) and other non-sparse (but regularised) probabilistic classifiers (regularised multinominal logistic regression, RMLR). The GRBFNN models were found to be better than the alternative RBFNNs for almost all datasets, producing the highest mean accuracy rank.

Details

ISSN :
14337479 and 14327643
Volume :
17
Database :
OpenAIRE
Journal :
Soft Computing
Accession number :
edsair.doi...........ec5e4628af557b76038f43a442a75123
Full Text :
https://doi.org/10.1007/s00500-012-0923-4