| Resumo: | 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.
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