Generalised Gaussian Radial Basis Function Neural Networks

Áreas de investigación:

• Methodology - Machine learning

Año:

2013

Tipo de publicación:

Artículo

Autores:

• Fernandez-Navarro, Francisco
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Journal:

Soft Computing

Volumen:

17

Número:

3

Páginas:

519-533

BibTex:

@article{SOCO2012i22fenaf, author = "Francisco Fernandez-Navarro and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", 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 (GGD). 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 $\tau$. In the proposed methodology, a Hybrid Evolutionary Algorithm (HEA) is employed to estimate the number of hidden neuron, the centers, 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 twenty 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. ", awards = "JCR (2013): 1.304 (category COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE, position 59/121 Q2)", comments = "JCR (2013): 1.304 (category COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE, position 59/121 Q2)", doi = "10.1007/s00500-012-0923-4", journal = "Soft Computing", note = "JCR (2013): 1.304 (category COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE, position 59/121 Q2)", number = "3", pages = "519-533", title = "{G}eneralised {G}aussian {R}adial {B}asis {F}unction {N}eural {N}etworks", url = "http://dx.doi.org/10.1007/s00500-012-0923-4", volume = "17", year = "2013", }

Nota:

JCR (2013): 1.304 (category COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE, position 59/121 Q2)

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 (GGD). 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 $\tau$. In the proposed methodology, a Hybrid Evolutionary Algorithm (HEA) is employed to estimate the number of hidden neuron, the centers, 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 twenty 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.

Comentarios:

JCR (2013): 1.304 (category COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE, position 59/121 Q2)