Advertisement

Generalized RBF Neural Network and FEM for Material Characterization Through Inverse Analysis

  • Tarik Hacib
  • Mohammed Rachid Mekideche
  • Fouzia Moussouni
  • Nassira Ferkha
  • Stéphane Brisset
Part of the Studies in Computational Intelligence book series (SCI, volume 119)

Abstract

This paper describes a new methodology for using artificial neural networks (ANN) and finite element method (FEM) in an electromagnetic inverse problem (IP) of parameters identification. The approach is used to identify unknown parameters of ferromagnetic materials. The methodology used in this study consists in the simulation of a large number of parameters in a material under test, using the FEM. Both variations in relative magnetic permeability and electric conductivity of the material under test are considered. Then, the obtained results are used to generate a set of vectors for the training of generalized radial basis function neural networks (RBFNN). Finally, the obtained neural network (NN) is used to evaluate a group of new materials, simulated by the FEM, but not belonging to the original dataset. The reached results demonstrate the efficiency of the proposed approach.

Keywords

Finite Element Method Hide Node Radial Basis Function Neural Network Electromagnetic Parameter Training Data Point 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    S. R. H. Hoole, Artificial neural networks in the solution of inverse electromagnetic field problems, IEEE Trans. Magn., 29(2), pp. 1931–1934, 1993.CrossRefGoogle Scholar
  2. 2.
    S. Haykin, Neural networks: a comprehensive foundation, Englewood Cliffs, NJ: Prentice-Hall, 1999.zbMATHGoogle Scholar
  3. 3.
    A. Fanni and A. Montisci, A neural inverse problem approach for optimal design, IEEE Trans. Magn., 39(3), pp. 1305–1308, 2003.CrossRefGoogle Scholar
  4. 4.
    P. Ramuhalli, L. Udpa and S.S. Udpa, Finite element neural networks for solving differential equations, IEEE Trans. Neural Netw., 16(6), pp. 1381–1392, 2005.CrossRefGoogle Scholar
  5. 5.
    J. A. Hartigan, Clustering Algorithms, John Wiley and Sons, Agnetism, CA: Academic, 2000.Google Scholar
  6. 6.
    P. P. Silvester and R. L. Ferrari, Finite Elements for Electrical Engineers. Cambridge, University Press, 1996.Google Scholar
  7. 7.
    M. T. Hagan and M. Menhaj, Training feed-forward networks with the Levenberg–Marquardt algorithm, IEEE Trans. Neural Netw., 5(6), pp. 989–993, 1994.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tarik Hacib
    • 1
  • Mohammed Rachid Mekideche
    • 1
  • Fouzia Moussouni
    • 2
  • Nassira Ferkha
    • 1
  • Stéphane Brisset
    • 2
  1. 1.Laboratoire L.A.M.E.LUniversité de JijelOuled Aissa JijelAlgeria
  2. 2.Laboratoire L2EP – Ecole Centrale de LilleCité ScientifiqueVilleneuve d’Ascq cedexFrance

Personalised recommendations