Volume 37 Issue 12
Dec.  2012
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Zhang Xian, Wang Hongli. Time series prediction using neuron-expanding regularized extreme learning machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(12): 1510-1514. (in Chinese)
Citation: Zhang Xian, Wang Hongli. Time series prediction using neuron-expanding regularized extreme learning machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(12): 1510-1514. (in Chinese)

Time series prediction using neuron-expanding regularized extreme learning machine

  • Received Date: 23 Jul 2010
  • Publish Date: 30 Dec 2012
  • A new algorithm called neuron-expanding regularized extreme learning machine (NERELM) was proposed and applied to time series prediction. In order to enhance the generalization performance of NERELM, the empirical risk and the structural risk were balanced on the basis of structural risk minimization theory. The output weights of NERELM were analytically determined at extremely fast learning speed instead of using gradient-based learning algorithm, and the optimal network structure of NERELM was automatically determined by expanding its hidden neuron nodes iteratively. Experiments on time series prediction indicate that NERELM has better performance in training computation cost and prediction accuracy in comparison with conventional gradient-based neural networks.

     

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