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.
KeywordsRadial Basis Function Neural Network Levenberg Marquardt Absolute Average Error Structure Selection Therapeutic Ultrasound
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- Ferreira, P. M., Ruano, A. E., Fonseca, C. M. (2003) Genetic assisted selection of RBF model structures for greenhouse inside air temperature prediction. In Proc. IEEE Conference on Control Applications. Vol 1 and 2. Instanbul, Turkey, pp. 576–581.Google Scholar
- Fonseca, C. M., Fleming P. J. (1993) Genetic algorithms for multi-objective optimization: Formulation, discution and generalization. In: Proc. 5th Int. Conf. Genetic Algorithms, Forrest, S. (eds.), pp. 416–423.Google Scholar
- Ferreira, P. M., Ruano, A. E. (2004) Predicting solar radiation with RBF neural networks. In: Proc. 6th Portuguese Conf. on Automatic Control, Vol. One, pp. 31–36.Google Scholar
- Billings, S., Voon, W. (1986) Correlation based model validity tests for non-linear models. International Journal of Control 44: 235–244.Google Scholar