Median M-Type Radial Basis Function Neural Network

  • José A. Moreno-Escobar
  • Francisco J. Gallegos-Funes
  • Volodymyr I. Ponomaryov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

In this paper we present the capability of the Median M-Type Radial Basis Function (MMRBF) Neural Network in image classification applications. The proposed neural network uses the Median M-type (MM) estimator in the scheme of radial basis function to train the neural network. Other RBF based algorithms were compared with our approach. From simulation results we observe that the MMRBF neural network has better classification capabilities

Keywords

Radial Basis Functions Rank M-type estimators Neural Networks 

References

  1. 1.
    Haykin, S.: Neural Networks, a Comprehensive Foundation. Prentice-Hall, Englewood Cliffs (1994)MATHGoogle Scholar
  2. 2.
    Rojas, R.: Neural Networks: A Systematic Introduction. Springer, Berlin (1996)Google Scholar
  3. 3.
    Gallegos, F., Ponomaryov, V.: Real-time image filtering scheme based on robust estimators in presence of impulsive noise. Real Time Imaging. 8(2), 78–90 (2004)Google Scholar
  4. 4.
    Gallegos-Funes, F., Ponomaryov, V., De-La Rosa, J., ABST,: M-type K-nearest neighbor (ABSTM-KNN) for image denoising. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E88-A(3), 798–799 (2005)Google Scholar
  5. 5.
    Bors, A.G., Pitas, I.: Median radial basis function neural network. IEEE Trans. Neural Networks. 7(6), 1351–1364 (1996)CrossRefGoogle Scholar
  6. 6.
    Bors, A.G., Pitas, I.: Object classification in 3-D images using alpha-trimmed mean radial basis function network. IEEE Trans. Image Process. 8(12), 1744–1756 (1999)CrossRefGoogle Scholar
  7. 7.
    Buhmann, M.D.: Radial Basis Functions: Theory and Implementations. Cambridge Monographs on Applied and Computational Mathematics (2003)Google Scholar
  8. 8.
    Karayiannis, N.B., Weiqun Mi, G.: Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques. IEEE Trans. Neural Networks. 8(6), 1492–1506 (1997)CrossRefGoogle Scholar
  9. 9.
    Karayiannis, N.B., Randolph-Gips, M.M.: On the construction and training of reformulated radial basis function neural networks. IEEE Trans. Neural Networks. 14(4), 835–846 (2003)CrossRefGoogle Scholar
  10. 10.
    Ritter, G.: Handbook of Computer Vision Algorithms in Image Algebra. CRC Press, Boca Raton-New York (2001)MATHGoogle Scholar
  11. 11.
    Myler, H.R., Weeks, A.R.: The Pocket Handbook of Image Processing Algorithms in C. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  12. 12.
    Musavi, M.T., Ahmed, W., Chan, K.H., Faris, K.B., Hummels, D.M.: On the training of radial basis function classifiers. Neural Networks. 5, 595–603 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • José A. Moreno-Escobar
    • 1
  • Francisco J. Gallegos-Funes
    • 1
  • Volodymyr I. Ponomaryov
    • 2
  1. 1.National Polytechnic Institute of Mexico, Mechanical and Electrical Engineering Higher School, Av. IPN s/n, U.P.A.L.M. SEPI-ESIME, Edif. Z, Acceso 3, Tercer Piso, Col. Lindavista, 07738, Mexico, D. F.Mexico
  2. 2.National Polytechnic Institute of Mexico, Mechanical and Electrical Engineering Higher School, Av. Santa Ana 1000, Col. San Francisco Culhuacan, 04430, Mexico, D. F.Mexico

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