Prediction of Chaotic Time Series of RBF Neural Network Based on Particle Swarm Optimization

  • Baoxiang Du
  • Wei Xu
  • Bingbing Song
  • Qun Ding
  • Shu-Chuan Chu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 298)

Abstract

Radial basis function (RBF) neural network has very good performance on prediction of chaotic time series, but the precision of prediction is great affected by embedding dimension and delay time of phase-space reconstruction in the process of predicting. Based on hereinbefore problems, we comprehensive optimize embedding dimension and delay time by particle swarm optimization, to get the optimal values of embedding dimension and delay time in RBF single-step and multi-step prediction models. In addition, we made single step and multi-step prediction to the Lorenz system by this method, the results show that the prediction accuracy of optimized prediction model is obvious improved.

Keywords

radial basis function (RBF) neural network particle swarm optimization prediction 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Baoxiang Du
    • 1
  • Wei Xu
    • 2
  • Bingbing Song
    • 1
  • Qun Ding
    • 1
  • Shu-Chuan Chu
    • 3
  1. 1.School of Electronic EngineeringHeilongjiang UniversityHarbinChina
  2. 2.School of Computer Science and TechnologyHeilongjiang UniversityHarbinChina
  3. 3.School of Computer Science, Engineering and MathematicsFlinders UniversityAdelaideAustralia

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