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

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Adaptive and Natural Computing Algorithms

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.

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References

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Author information

Authors and Affiliations

  1. Centre for Intelligent Systems, University of Algarve, Portugal

    C. A. Teixeira, A. E. Ruano & M. Graça Ruano

  2. Biomedical Eng. Program - COPPE/ Federal University of Rio de Janeiro, Brazil

    W. C. A. Pereira

Authors
  1. C. A. Teixeira
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  2. W. C. A. Pereira
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  3. A. E. Ruano
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  4. M. Graça Ruano
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Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Universidade de Coimbra, Coimbra, Portugal

    Bernardete Ribeiro

  2. Institut für Informatik, Universität Innsbruck, Innsbruck, Austria

    Rudolf F. Albrecht

  3. Fakulteta za Računalništvo in Informatiko, Univerza v Ljubljani, Ljubljana, Slovenia

    Andrej Dobnikar

  4. Equipe Universitaire de Recherche en Informatique de Saint-Etienne (Groupe de Recherche de Roanne) Institut Universitaire de Technologie de Roanne, Université Jean Monnet, Saint-Etienne, France

    David W. Pearson

  5. Division of Mathematics School of Mathematical and Information Sciences, Coventry University, Coventry, UK

    Nigel C. Steele

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© 2005 Springer-Verlag/Wien

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Teixeira, C.A., Pereira, W.C.A., Ruano, A.E., Ruano, M.G. (2005). Multi-objective genetic algorithm applied to the structure selection of RBFNN temperature estimators. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_122

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  • DOI: https://doi.org/10.1007/3-211-27389-1_122

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-24934-5

  • Online ISBN: 978-3-211-27389-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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