@misc{10481/77890,
year = {2006},
url = {https://hdl.handle.net/10481/77890},
abstract = {Radial Basis Function Neural Networks (RBFNNs) are well
known because, among other applications, they present a good perfor-
mance when approximating functions. The function approximation prob-
lem arises in the construction of a control system to optimize the process
of the mineral reduction. In order to regulate the temperature of the
ovens and other parameters, it is necessary a module to predict the ¯nal
concentration of mineral that will be obtained from the source materials.
This module can be formed by an RBFNN that predicts the output and
by the algorithm that designs the RBFNN dynamically as more data is
obtained. The design of RBFNNs is a very complex task where many
parameters have to be determined, therefore, a genetic algorithm that
determines all of them has been developed. This algorithm provides sat-
isfactory results since the networks it generates are able to predict quite
precisely the ¯nal concentration of mineral.},
organization = {Spanish
CICYT Project TIN2004-01419},
organization = {European Commission's Research Infrastructures RII3-CT-2003-506079 (HPC-Europa)},
publisher = {Springer},
keywords = {Inteligencia artificial},
keywords = {Artificial intelligence},
title = {Multiobjective RBFNNs Designer for Function Approximation: An Application for Mineral Reduction},
author = {Guillén Perales, Alberto and Rojas Ruiz, Ignacio and González Peñalver, Jesús and Pomares Cintas, Héctor Emilio and Herrera Maldonado, Luis Javier and Fernández Baldomero, Francisco J.},
}