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Prediction of multivariate chaotic time series via radial basis function neural network

Authors

  • Diyi Chen,

    Corresponding author
    1. Department of Electrical Engineering, Northwest A&F University, Shaanxi Yangling 712100, China
    2. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287
    3. Institute of Efficient Water Use for Arid Agriculture of China Northwest A&F University, Shaanxi Yangling 712100, China
    • Institute of Efficient Water Use for Arid Agriculture of China Northwest A&F University, Shaanxi Yangling 712100, China
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  • Wenting Han

    Corresponding author
    1. Institute of Efficient Water Use for Arid Agriculture of China Northwest A&F University, Shaanxi Yangling 712100, China
    2. Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Shaanxi Yangling 712100, China
    • Institute of Efficient Water Use for Arid Agriculture of China Northwest A&F University, Shaanxi Yangling 712100, China
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Abstract

In this article, a new multivariate radial basis functions neural network model is proposed to predict the complex chaotic time series. To realize the reconstruction of phase space, we apply the mutual information method and false nearest-neighbor method to obtain the crucial parameters time delay and embedding dimension, respectively, and then expand into the multivariate situation. We also proposed two the objective evaluations, mean absolute error and prediction mean square error, to evaluate the prediction accuracy. To illustrate the prediction model, we use two coupled Rossler systems as examples to do simultaneously single-step prediction and multistep prediction, and find that the evaluation performances and prediction accuracy can achieve an excellent magnitude. © 2013 Wiley Periodicals, Inc. Complexity, 2013.

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