| 基于径向基神经网络预测的混沌时间序列嵌入维数估计方法 |
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| 引用本文： | 李鹤,杨周,张义民,闻邦椿.基于径向基神经网络预测的混沌时间序列嵌入维数估计方法[J].物理学报,2011,60(7):70512-070512. |
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| 作者姓名： | 李鹤 杨周 张义民 闻邦椿 |
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| 作者单位： | 东北大学机械工程与自动化学院,沈阳 110819 |
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| 基金项目： | 教育部新世纪优秀人才支持计划(批准号:NCET-10-0271),机械系统与振动国家重点实验室开放课题(批准号:MSV-2011-19)教育部长江学者与创新团队发展计划(批准号:IRT0816),国家自然科学基金(批准号:10702014),中央高校基本科研业务费专项资金(批准号:N100503002,N100703001)资助的课题. |
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| 摘 要： | 根据Takens定理,研究了混沌时间序列相空间重构嵌入维数的选取问题.提出了基于径向基函数神经网络预测模型性能的嵌入维数估计方法,即根据嵌入维数与混沌时间序列预测模型性能的变化关系来确定嵌入维数.通过对几种典型混沌动力学系统的数值验证,结果表明该方法能够确定出合适的相空间重构嵌入维数.
关键词：
混沌
相空间重构
嵌入维数
预测
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| 关 键 词： | 混沌 相空间重构 嵌入维数 预测 |
| 收稿时间： | 7/9/2010 12:00:00 AM |
Methodology of estimating the embedding dimension in chaos time series based on the prediction performance of radial basis function neural networks |
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| Li He,Yang Zhou,Zhang Yi-Min and Wen Bang-Chun.Methodology of estimating the embedding dimension in chaos time series based on the prediction performance of radial basis function neural networks[J].Acta Physica Sinica,2011,60(7):70512-070512. |
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| Authors: | Li He Yang Zhou Zhang Yi-Min and Wen Bang-Chun |
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| Institution: | School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China;School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China;School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China;School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China |
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| Abstract: | We have studied the methodology of estimating the embedding dimension for phase space reconstruction of chaotic time series according to the Takens theorem. We present an approach to the estimation of the embedding dimension based on the prediction of nonlinear performance. That is, we determine the embedding dimension by considering the variation of the performance of prediction model of chaotic time series with embedding dimension. Numerical simulations verify that the method is applicable for determining an appropriate embedding dimension. |
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| Keywords: | chaos phase space reconstruction embedding dimension prediction |
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