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Volume 22 Issue 6
Nov.  2000
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Zhang Song, Wang Yuanmei . NONLINEAR TIME SERIES PREDICTOR BASED ON GENERALIZED RADIAL BASIS FUNCTION NEURAL NETWORKS[J]. Journal of Electronics & Information Technology, 2000, 22(6): 965-971.
Citation: Zhang Song, Wang Yuanmei . NONLINEAR TIME SERIES PREDICTOR BASED ON GENERALIZED RADIAL BASIS FUNCTION NEURAL NETWORKS[J]. Journal of Electronics & Information Technology, 2000, 22(6): 965-971.

NONLINEAR TIME SERIES PREDICTOR BASED ON GENERALIZED RADIAL BASIS FUNCTION NEURAL NETWORKS

  • Received Date: 1999-02-11
  • Rev Recd Date: 1999-11-29
  • Publish Date: 2000-11-19
  • The architecture and learning algorithm of traditional radial basis function (RBF) neural networks are surveyed in this paper. A generalized radial basis function model is proposed, which is more flexible and extensible. Based on the numerical solution to Mackey-Glass hematopoietic model equation, the prediction results obtained by radial basis function (RBF) model, gradient radial basis function (GRBF) model, and the generalized radial basis function model are compared and discussed, which show the effectiveness of the generalized model.
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  • Park J, Sandberg I W. Approximation and radial-basis-function networks[J].Neural Computation.1993, 5(2):305-316[2]Chen S, Cowan F N, Grant P M. Orthogonal least square learning algorithm for radial basis function networks[J].IEEE Trans. on Neural Networks.1991, 2(2):302-309[3]Casdagli M. Nonlinear prediction of chaotic time series[J].Physica D.1989, 35(3):335-356[4]Chng E S, Chen S, Mulgrew B. Gradient radial basis function networks for nonlinear and nonstationary time series prediction[J].IEEE Trans. on Neural Networks.1996, 7(1):190-194[5]Mackey M C, Glass L. Oscillation and chaos in physiology control systems[J].Science.1977,197(4300):287-289
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