Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Determining the Number and the Locations of RBF Centers Using Enhanced K-Medoids Clustering and Bi-Section Search Method

보정된 K-medoids 군집화 기법과 이분 탐색기법을 이용한 RBF 네트워크의 중심 개수와 위치와 통합 결정

  • Lee, Daewon (Department of Industrial Engineering, Pohang University of Science and Technology) ;
  • Lee, Jaewook (Department of Industrial Engineering, Pohang University of Science and Technology)
  • 이대원 (포항공과대학교 산업공학과) ;
  • 이재욱 (포항공과대학교 산업공학과)
  • Published : 2003.06.30
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Abstract

In the recent researches, a variety of ways for determining the locations of RBF centers have been proposed assuming that the number of RBF centers is known. But they have also many numerical drawbacks. We propose a new method to overcome such drawbacks. The strength of our method is to determine the locations and the number of RBF centers at the same time without any assumption about the number of RBF centers. The proposed method consists of two phases. The first phase is to determine the number and the locations of RBF centers using bi-section search method and enhanced k-medoids clustering which overcomes drawbacks of clustering algorithm. In the second phase, network weights are computed and the design of RBF network is completed. This new method is applied to several benchmark data sets. Benchmark results show that the proposed method is competitive with the previously reported approaches for center selection.

Keywords

References

  1. A. C. Micchelli (1986), Interpolation of scattered data: Distance matrices and conditionally positive definite functions, Construct. Approx, vol. 2, pp. 11-22
  2. C. M. Bishop (1991), Improving the generalization properties of radial basis function neural networks, Neural Computation, vol. 3(4), pp. 579-588
  3. Cover, T.M. (1965), Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition, IEEE Trans. Electronic Computers, vol. EC-14, pp. 326-334
  4. Duda, R.O., and P.E. Hart (1973), Pattern classification and Scene Analysis, New York: Wiley
  5. D. S. Broomhead and D. Lowe (1988), Multivariable functional interpolation and adaptive networks, Complex Syst., vol. 2, pp. 321-355
  6. J. Barry Gomm and Ding Li Yu (2000), Selecting Radial Basis Function Network Centers with Recursive Orthogonal Least Squares Training, IEEE Trans. Neural Networks, vol. 11(2). pp.306-314
  7. K. Z. Mao (2002), RBF Neural Network Center Selection Based on Fisher Ratio Class Separability Measure, IEEE Trans. Neural Network. vol. 13(5), pp. 1211-1217
  8. L. Bruzzone and D. F. Prieto (1999), A technique for the selection of kernel function parameters in RBF neural networks for classification of remotesensing images, IEEE Trans. Geosci., vol. 31(2), pp. 1179-1184
  9. Lowe (1989), Adaptive radial basis function nonlinearities, and the problem of generalisation, First IEE International Conference on Artificial Neural Networks, pp.171-175, London
  10. M. J. Orr (1995), Regularization in the selection of RBF centers, Neural Computation, vol. 1(3), pp. 606-623
  11. M. J. D. Powell (1985), Radial basis functions for multi variable interpolation: A review, in Proc. IMA Conf. Algorithms for the Approximation of Functions and Data. Shrivenham. U.K.
  12. Poggio and Girosi, (1990), Networks for approximation and learning, Proceedings of the IEEE, vol.18, pp. 1481-1497
  13. S. Chen (1995), Nonlinear time series modeling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning, Inst. Elect Eng. Electron. Lett., vol. 31(2). pp. 117-118
  14. Wettschereck and Diettcrich (1992), Improving the performance of radial basis function networks by learning center locations, Advances in Neural Information Processing Systems, vol. 4, pp.1133-1140, San Mateo, CA : Morgan Kaufmann
  15. Zheng ou Wang and Tao Zhu (2000). Al efficient learining algorithm for improving generalization performance of radial basis function neural networks, Neural Networks, vol. 13(4,5), 2000
  16. Z. Uykan, C. Guzelis, M. E. Celebi and H. N. Koivo (2000), Analysis of inputoutput clustering for determining centers of RBFN, IEEE Trans. Neural Networks, vol. 11, pp. 851-858