Evolutionary Optimization of RBF Networks

13 years 9 months ago

Download sci2s.ugr.es

One of the main obstacles to the widespread use of artijcial neural networks is the difJiculty of adequately define valuesfor their free parameters. This article discusses how Radial Basis Function, RBF; networks can have their parameters defined by genetic algorithms. For such, it presents an overall view of the problems involved and the different approaches used to genetically optimize RBF networks. Finally, a model is proposed which includes representation, crossover operator and multiobjective optimization criteria. Experimental results using this model are presented.

Estefane G. M. de Lacerda, Teresa Bernarda Ludermi

Real-time Traffic

Artijcial Neural Networks | Neural Networks | Radial Basis Function | Rbf Networks | SBRN 2000 |

claim paper

» Evolutionary Radial Basis Functions for Credit Assessment

» Evolutionary strategies for multiscale radial basis function kernels in support vector mac...

» A pareto archive evolutionary strategy based radial basis function neural network training...

» Evolving RBF Neural Networks

» Classification of Polarimetric SAR Images Using Evolutionary RBF Networks

» Classification of Polarimetric SAR Images Using Evolutionary RBF Neural Networks

» Parallel radial basis function methods for the global optimization of expensive functions

» Radial basis functionbased image segmentation using a receptive field

Post Info
More Details (n/a)

Added	01 Aug 2010
Updated	01 Aug 2010
Type	Conference
Year	2000
Where	SBRN
Authors	Estefane G. M. de Lacerda, Teresa Bernarda Ludermir, André Carlos Ponce Leon Ferreira de Carvalho

Comments (0)

Sciweavers

Evolutionary Optimization of RBF Networks

Artijcial Neural Networks | Neural Networks | Radial Basis Function | Rbf Networks | SBRN 2000 |

Explore & Download

Productivity Tools

Document Tools

Image Tools

Sciweavers