论文信息 - Multi-objective genetic algorithm applied to the structure selection of RBFNN temperature estimators

Multi-objective genetic algorithm applied to the structure selection of RBFNN temperature estimators

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 homo-geneous 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 0.0084°C was attained with these estimators.

António E. Ruano | Wagner Coelho de Albuquerque Pereira | Cesar Teixeira | M. Graca Ruano

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