Multiobjective RBFNNs Designer for Function Approximation: An Application for Mineral Reduction
Identificadores
URI: https://hdl.handle.net/10481/77890Metadatos
Mostrar el registro completo del ítemAutor
Guillén Perales, Alberto; Rojas Ruiz, Ignacio; González Peñalver, Jesús; Pomares Cintas, Héctor Emilio; Herrera Maldonado, Luis Javier; Fernández Baldomero, Francisco J.Editorial
Springer
Materia
Inteligencia artificial Artificial intelligence
Fecha
2006Referencia bibliográfica
Published version: Guillén, A... [et al.] (2006). Multiobjective RBFNNs Designer for Function Approximation: An Application for Mineral Reduction. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/11881070_71]
Patrocinador
Spanish CICYT Project TIN2004-01419; European Commission's Research Infrastructures RII3-CT-2003-506079 (HPC-Europa)Resumen
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.