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A Fast Robust Learning Algorithm for RBF Network Against Outliers

  • Mei-juan Su
  • Wei Deng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

Training data set often contains outliers, which can cause substantial deterioration of the approximation realized by a neural network. In this paper, a fast robust learning algorithm against outliers for RBF network is presented. The algorithm uses the subtractive clustering(SC) method to select hidden node centers of RBF network, and the gradient descent method with the scaled robust loss function(SRLF) as the objective function to adjust hidden node widths and the connection weights of the network. Therefore, the learning of RBF network has robustness on dealing with outliers and fast rate of convergence. The experimental results show the advantages of the learning algorithm over traditional learning algorithms for RBF network.

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References

  1. 1.
    Haykin, S.: Neural Networks, A Comprehensive Foundation, 2nd edn., pp. 298–305. Tsinghua University Press & Prentice Hall (2001)Google Scholar
  2. 2.
    Moody, J., Darken, C.: Fast Learning in Networks of Locally-tuned Processing Units. Neural Computation 1(2), 281–294 (1989)CrossRefGoogle Scholar
  3. 3.
    Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Transactions on Neural Networks 2(2), 302–309 (1991)CrossRefGoogle Scholar
  4. 4.
    Guang-Bin, H., Saratchandran, P., Narasimhan, S.: A Generalized Growing and Pruning RBF Neural Network for Function Approximation. IEEE Transactions on Neural Networks 16(1), 57–67 (2005)CrossRefGoogle Scholar
  5. 5.
    Sánchez, V.D.A.: Robustization of a learning method for RBF networks. Neurocomputing 9, 85–94 (1995)CrossRefGoogle Scholar
  6. 6.
    Chien-Cheng, L., Pau-Choo, C., Jea-Rong, T., Chein-I, C.: Robust Radial Basis Function Neural Networks. IEEE Transactions on Systems, Man, and Cybernetics 29(6), 674–685 (1999)Google Scholar
  7. 7.
    Meiqin, L., Xiaoxin, L.: A Robust Learning Algorithm for RBF Neural Networks. Journal of Huazhong University of Science and Technology (Nature Science) 28(2), 8–10 (2000) (In Chinese)Google Scholar
  8. 8.
    Haralambos, S., Alex, A., George, B.: A Fast Training Algorithm for RBF Networks Based on Subtractive Clustering. Neurocomputing 51, 501–505 (2003)CrossRefGoogle Scholar
  9. 9.
    Hampel, F.R., Rousseeuw, P.J., Ronchetti, E.: The Change-of-variance Curve and Optimal Redescending M-estimators. J. American Statistical Assoc. 761, 643–648 (1981)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A.: Robust Statistics: The Approach Based on Influence Function. Wiley, New York (1986)zbMATHGoogle Scholar
  11. 11.
    David, S.C., Ramesh, C.J.: A Robust Back Propagation Learning Algorithm for Function Approximation. IEEE Transactions on Neural Networks 5(3), 467–479 (1994)CrossRefGoogle Scholar
  12. 12.
    Chiu, S.L.: Fuzzy Model Identification Based on Cluster Estimation. J. Intell. Fuzzy Systems 2(3), 267–278 (1994)Google Scholar
  13. 13.
    Weixiang, Z., Dezhao, C., Shangxu, H.: Detection of Outlier and A Robust BP Algorithm Against Outlier. Computers and Chemical Engineering 28, 1403–1408 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mei-juan Su
    • 1
  • Wei Deng
    • 1
  1. 1.School of Computer Science & TechnologySoochow UniversitySuzhouChina

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