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An application of Parallel Radial Basis Function (PRBF) network model on prediction of chaotic time series is presented in this paper. The PRBF net consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of PRBF is a weighted...
This paper discusses a method for chaotic time series prediction based on Parallel Multi-Layer Perceptron (PMLP) neural network. The number of input nodes for PMLP is determined by embedding dimension based on chaotic phase-space reconstruction. Both Grassberger-Procaccia algorithm and Takens' method are employed to calculate minimal embedding dimension of chaotic time series. Finally, the prediction...
This paper discusses a method for chaotic time series prediction based on radial basis function (RBF) neural network. The number of input nodes for RBF is determined by embedding dimension based on chaotic phase-space reconstruction. Both Grassberger--Procaccia algorithm and Takens' method are employed to calculate minimal embedding dimension of chaotic time series. Finally, the prediction accuracy...
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