Non-invasive time-spatial temperature simulation using neural networks

Teixeira, C. A.; Ruano, M. Graça; Ruano, Antonio; Pereira, W. C. A.

http://hdl.handle.net/10400.1/2248

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Abstract(s)

In this paper the performance of radial basis functions neural networks is accessed for non-invasive time-spatial temperature simulation in a gel-based phantom. The medium was heated at different intensities with a physiotherapeutic ultrasound device. In order to find an appropriate neural network structure the multi-objective genetic algorithm was applied. After the structure selection phase a set of preferable individuals was obtained, and the best one presents a maximum absolute error less than 0.5 oC, as desired in hyperthermia. In addition this model has low computational complexity, a fundamental point for a real-time application.

Keywords

Temperature profiles Temperature control Neural networks-models Radial base function networks Multiobjective optimisation

URI

http://hdl.handle.net/10400.1/2248

Citation

Teixeira, C. A.; Ruano, M. G.; Ruano, A. E. Non-Invasive Time-Spatial Temperature Simulation using Neural Networks, Trabalho apresentado em 7th Portuguese Conference on Automatic Control (Controlo 2006), In Proceedings of the 7th Portuguese Conference on Automatic Control (Controlo 2006), Aveiro, 2006.