| 基于小波神经网络的混沌时间序列预测及应用 |
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| 引用本文： | 陶小创,樊焕贞,吕琛,王自力.基于小波神经网络的混沌时间序列预测及应用[J].南京航空航天大学学报,2011(Z1). |
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| 作者姓名： | 陶小创 樊焕贞 吕琛 王自力 |
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| 作者单位： | 北京航空航天大学可靠性与系统工程学院; |
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| 基金项目： | 国家自然科学基金(61074083,50705005)资助项目; 国防技术基础资助项目 |
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| 摘 要： | 根据相空间重构理论,探讨了一种基于小波神经网络(WNN)的混沌时间序列预测方法。根据G-P算法和Takens理论,计算出混沌时间序列相空间重构所需的最小嵌入维数,以此作为网络的输入节点数。通过时频分析,使得隐节点数的选取也有了可靠的理论依据。最后对Lorenz仿真信号和滚动轴承信号进行仿真和预测,验证了方法的有效性。结果表明,对于混沌时间序列的预测,WNN网络比BP网络表现出更理想的预测效果,为非线性动态系统的预测提供了一种有效的途径。
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| 关 键 词： | 小波神经网络 混沌时间序列 相空间重构 |
Chaotic Time Series Prediction and Its Application Based on Wavelet Neural Network |
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| Tao Xiaochuang,Fan Huanzhen,Lu Chen,Wang Zili.Chaotic Time Series Prediction and Its Application Based on Wavelet Neural Network[J].Journal of Nanjing University of Aeronautics & Astronautics,2011(Z1). |
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| Authors: | Tao Xiaochuang Fan Huanzhen Lu Chen Wang Zili |
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| Institution: | Tao Xiaochuang,Fan Huanzhen,Lu Chen,Wang Zili(School of Reliability and Systems Engineering,Beihang University,Beijing,100191,China) |
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| Abstract: | A method for chaotic time series prediction based on wavelet neural network is discussed by analyzing the theory of phase space reconstruction.G-P algorithm and Takens theory are applied to calculate the minimum embedding dimensions which are required by the phase space reconstruction of chaotic time series and will be used as the number of input nodes.Through the time-frequency analysis,the number of hidden nodes can also be determined on a reliable theoretical basis.Finally,the chaotic time series data fr... |
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| Keywords: | wavelet neural network(WNN) chaotic time series phase space reconstruction |
| 本文献已被 CNKI 等数据库收录！ |
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