Temperature modelling of an homogeneous medium using genetically selected RBF(LIC)
| Authors: | Teixeira César, Universidade do Algarve/Centre for Intelligent Systems, Portugal
Graça Ruano Maria, Universidade do Algarve/Centre for Intelligent Systems, Portugal
Pereira Wagner, Universidade Federal do Rio de Janeiro/COPPE, Brazil
Ruano António, Universidade do Algarve/Centre for Intelligent Systems, Portugal |
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| Topic: | 8.2 Modelling & Control of Biomedical Systems |
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| Session: | Parameter Estimation and Kinetic Modelling II |
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| Keywords: | Biomedical systems, Temperature profiles, Neural network-models, Radial base function networks, Multiobjective optimisations. |
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Abstract
Temperature modelling of human tissue exposed to therapeutic ultrasound is essential for an accurate instrumental assessment and calibration. In this paper punctual temperature modelling of an homogeneous medium, radiated by therapeutic ultrasound, is presented. Two different approaches are considered: a completely nonlinear approach (Radial Basis Functions neural networks - RBF), and a hybrid (Linear plus nonlinear) approach (Radial Basis Functions neural networks with Linear Input Connections - RBFLIC). The best-performant Neural Network (NN) structures were obtained using a Multi-Objective Genetic Algorithm (MOGA). The best RBFLIC structure for the applied MOGA parametrisation, presents 28% improvement in the performance of the best RBF structure.
