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Multi-objective genetic algorithm applied to the structure selection of RBFNN temperature estimators

  • C. A. Teixeira
  • W. C. A. Pereira
  • A. E. Ruano
  • M. Graça Ruano

Abstract

Temperature modelling of a homogeneous medium, when this medium is radiated by therapeutic ultrasound, is a fundamental step in order to analyse the performance of estimators for in-vivo modelling. In this paper punctual and invasive temperature estimation in a homo-geneous medium is employed. Radial Basis Functions Neural Networks (RBFNNs) are used as estimators. The best fitted RBFNNs are selected using a Multi-objective Genetic Algorithm (MOGA). An absolute average error of 0.0084°C was attained with these estimators.

Keywords

Radial Basis Function Neural Network Levenberg Marquardt Absolute Average Error Structure Selection Therapeutic Ultrasound 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • C. A. Teixeira
    • 1
  • W. C. A. Pereira
    • 2
  • A. E. Ruano
    • 1
  • M. Graça Ruano
    • 1
  1. 1.Centre for Intelligent SystemsUniversity of AlgarvePortugal
  2. 2.Biomedical Eng. Program - COPPE/ Federal University of Rio de JaneiroBrazil

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