| PREDICTING CHAOTIC TIME SERIES WITH IMPROVED LOCAL APPROXIMATIONS |
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| 引用本文： | MUXiaowu LINLan ZHOUXiangdong. PREDICTING CHAOTIC TIME SERIES WITH IMPROVED LOCAL APPROXIMATIONS[J]. 系统科学与复杂性, 2004, 17(2): 207-219 |
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| 作者姓名： | MUXiaowu LINLan ZHOUXiangdong |
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| 作者单位： | MU Xiaowu LIN Lan ZHOU Xiangdong (1. Department of Mathematics，Zhengzhou University，Zhengzhou 450052，China) (2. School of Electronics and Information Engineering，Tongji University，Shanghai 200092，China) (3.Department of Computing and Information Technology，Fudan University，Shanghai 200433，China) |
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| 摘 要： | In this paper, new approaches for chaotic time series prediction are introduced. We first summarize and evaluate the existing local prediction models, then propose optimization models and new algorithms to modify procedures of local approximations. The modification to the choice of sample sets is given, and the zeroth-order approximation is improved by a linear programming method. Four procedures of first-order approximation are compared, and corresponding modified methods are given. Lastly, the idea of nonlinear feedback to raise predicting accuracy is put forward. In the end, we discuss two important examples, i.e. Lorenz system and Rossler system, and the simulation experiments indicate that the modified algorithms are effective.
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| 关 键 词： | 混沌时间序列 局部逼近 线性规划 反馈控制 Lorenz吸引子 |
PREDICTING CHAOTIC TIME SERIES WITH IMPROVED LOCAL APPROXIMATIONS |
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| MU Xiaowu LIN Lan ZHOU Xiangdong. PREDICTING CHAOTIC TIME SERIES WITH IMPROVED LOCAL APPROXIMATIONS[J]. Journal of Systems Science and Complexity, 2004, 17(2): 207-219 |
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| Authors: | MU Xiaowu LIN Lan ZHOU Xiangdong |
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| Abstract: | In this paper, new approaches for chaotic time series prediction are introduced. We first summarize and evaluate the existing local prediction models, then propose optimization models and new algorithms to modify procedures of local approximations. The modification to the choice of sample sets is given, and the zeroth-order approximation is improved by a linear programming method. Four procedures of first-order approximation are compared, and corresponding modified methods are given. Lastly, the idea of nonlinear feedback to raise predicting accuracy is put forward. In the end, we discuss two important examples, i.e. Lorenz system and Rossler system, and the simulation experiments indicate that the modified algorithms are effective. |
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| Keywords: | Chaotic time series prediction local approximations linear programming nonlinear feedback. |
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