Time Series Prediction Based on Chaotic Attractor |
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Authors: | LI Ke-Ping CHEN Tian-Lun GAO Zi-You |
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Institution: | Institute of Systems Science, Northern Jiaotong University, Beijing 100044, China Department of Physics, Nankai University, Tianjin 300071, China Institute of Systems Science, Northern Jiaotong University, Beijing 100044, China |
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Abstract: | A new prediction technique is proposed for chaotic time series. The usefulness of the technique is thatit can kick off some false neighbor points which are not suitable for the local estimation of the dynamics systems. Atime-delayed embedding is used to reconstruct the underlying attractor, and the prediction model is based on the timeevolution of the topological neighboring in the phase space. We use a feedforward neural network to approximate thelocal dominant Lyapunov exponent, and choose the spatial neighbors by the Lyapunov exponent. The model is testedfor the Mackey-Glass equation and the convection amplitude of lorenz systems. The results indicate that this predictiontechnique can improve the prediction of chaotic time series. |
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Keywords: | chaotic time series neural network exponential divergence |
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