Regressive and non-regressive RBFNN estimators in non-invasive temperature estimation - Teixeira, C. A. (2013)
In this paper two modelling strategies were applied for non-invasive temperature estimation in a gel-based phantom subjected to physiotherapeutic ultrasound. The two strategies differ in the consideration of regressive or nonregressive radial basis functions neural networks (RBFNN) structures. The gel-based phantom was heated using four different ultrasound intensities. Temperature was monitored at five points inside the phantom, where temperature is to be estimated. The best regressive model reaches a mean maximum absolute error of 0.4 ºC, against 0.8 ºC presented by the best non-regressive model. The regressive strategy presented better error performance with a smaller computational complexity. Thus, being the appropriate one for real-time temperature monitoring in hyperthermia/diathermia procedures, where resolutions below 0.5 ºC are required.