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

    Research output: Contribution to conferencePaper

    5 Citations (Scopus)

    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.

    Original languageEnglish
    Pages489-497
    Number of pages9
    DOIs
    Publication statusPublished - 1 Jan 2014
    EventThe First Euro-China Conference on Intelligent Data Analysis and Applications - Shenzhen, China
    Duration: 13 Jun 201415 Jun 2014

    Conference

    ConferenceThe First Euro-China Conference on Intelligent Data Analysis and Applications
    CountryChina
    CityShenzhen
    Period13/06/1415/06/14

    Keywords

    • Neural network
    • Particle swarm optimization
    • Prediction
    • Radial basis function (RBF)

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  • Cite this

    Du, B., Xu, W., Song, B., Ding, Q., & Chu, S-C. (2014). Prediction of Chaotic Time Series of RBF Neural Network Based on Particle Swarm Optimization. 489-497. Paper presented at The First Euro-China Conference on Intelligent Data Analysis and Applications, Shenzhen, China. https://doi.org/10.1007/978-3-319-07773-4_48