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Multi-objective genetic algorithm applied to the structure selection of RBFNN temperature estimators
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Resumo(s)
Temperature modelling of a homogeneous medium, when this medium is radiated by therapeutic ultrasound, is a fundamental step in order to analyse the performance
of estimators for in-vivo modelling. In this paper punctual and invasive temperature estimation in a homogeneous medium is employed. Radial Basis Functions Neural Networks (RBFNNs) are used as estimators. The
best fitted RBFNNs are selected using a Multi-objective Genetic Algorithm (MOGA). An absolute average error of 0.0084oCwas attained with these estimators.
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Contexto Educativo
Citação
Teixeira, C. A.; Pereira, W. C. A.; Ruano, A. E.; Ruano, M. Graça. Multi-objective genetic algorithm applied to the structure selection of RBFNN temperature estimators, In Adaptive and Natural Computing Algorithms, 506-509, ISBN: 3-211-24934-6. Vienna: Springer-Verlag, 2005.