| Research on Chaotic Time Series Prediction Based on K-entropy and RBF Neural Networks |
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| 引用本文： | Xiu Yan Junhai Ma. Research on Chaotic Time Series Prediction Based on K-entropy and RBF Neural Networks[J]. 系统科学与信息学报, 2006, 4(4): 741-748 |
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| 作者姓名： | Xiu Yan Junhai Ma |
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| 作者单位： | [1]Management School, Tianjin University, Tianjin 300072, China [2]Department of Foundation, Tianjin Urban Construction Institute, Tianjin 300384, China |
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| 基金项目： | This work is supported by National Natural Science Foundation of China（70271071） and the Science and Technology Development Foundation of Tianjin Education Committee （20052171）. |
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| 摘 要： | ![]()
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| 关 键 词： | Kolmogorov熵 无序时间级数 RBF神经网络 预测 |
| 收稿时间： | 2006-03-24 |
Research on Chaotic Time Series Prediction Based on K-entropy and RBF Neural Networks |
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| Abstract: | ![]()
In this paper, a method of direct multi-step prediction of chaotic time series is proposed, which is based on Kolmogorov entropy and radial basis functions neural networks. This is done first by reconstructing a phase space using chaotic time series, then using K-entropy as a quantitative parameter to obtain the maximum predictability time of chaotic time series, finally the predicted chaotic time series data can be acquired by using RBFNN. The application considered is Lorenz system. Simulation results for direct multi-step prediction method are compared with recurrence multi-step prediction method. The results indicate that the direct multi-step prediction is more accurate and rapid than the recurrence multi-step prediction within the maximum predictability time of chaotic time series. So, it is convenient to forecast and control with real time using the method of direct multi-step prediction. |
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| Keywords: | Kolmogorov entropy chaotic time series RBF neural networks multi-step prediction |
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