Teixeira, C. A.Ruano, M. GraçaPereira, W. C. A.Ruano, AntonioNegreira, C.2013-02-062013-02-062006Teixeira, C. A.; Ruano, M. G.; Pereira, W. C. A.; Ruano, A. E.; Negreira, C. Linear versus non-linear non-invasive temperature predictors in a homogeneous medium subjected to physiotherapeutic ultrasound, Revista Brasileira de Engenharia Biomédica, 22, 2, 131-141, 2006.1517-3151AUT: MRU00118; ARU00698;http://hdl.handle.net/10400.1/2244The lack of accurate time-spatial temperature estimators/predictors conditions the safe application of thermal therapies, such as hyperthermia. In this paper, a comparison between a linear and a non-linear class of models for non-invasive temperature prediction in a homogeneous medium, subjected to ultrasound at physiotherapeutic levels is presented. The linear models used were autoregressive with exogenous inputs (ARX) and the non-linear models were radial basis functions neural networks (RBFNN). In order to create and validate the models, an experiment was build to extract in vitro ultrasound RF-lines, as well as its correspondent temperature values. Then, features were extracted from the measured RF-lines and the models were trained and validated. For both the models, the best-fitted structures were selected using the multi-objective genetic algorithm (MOGA), given the enormous number of possible structures. The best RBFNN model presented a maximum absolute predictive error in the validation set five times less than the value presented by the best ARX model. In this work, the best RBFNN reached a maximum absolute error of 0.42 ºC, which is bellow the value pointed as a borderline between an appropriate and an undesired temperature estimator, which is 0.5 ºC. The average error was one order of magnitude less in the RBFNN case, and a less biased estimation was met. In addition, the best RBFNN needed less environmental information (inputs), given the capacity to non-linearly relate the information. The results obtained are encouraging, considering that coherent results should be obtained in a time-spatial modelling schema using RBFNN models.engNon-invasive temperature estimationPhysiotherapeutic ultrasoundRadial basis functions neural networksMulti-objective genetic algorithmsjournal article2013-01-26