基于小波神经网络的混沌时间序列预测及应用  

Chaotic Time Series Prediction and Its Application Based on Wavelet Neural Network

作　　者：陶小创[1] 樊焕贞[1] 吕琛[1] 王自力[1] 

机构地区：[1]北京航空航天大学可靠性与系统工程学院,北京100191

出　　处：《南京航空航天大学学报》2011年第B07期174-178,共5页Journal of Nanjing University of Aeronautics & Astronautics

基　　金：国家自然科学基金(61074083;50705005)资助项目;国防技术基础资助项目

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。A method for chaotic time series prediction analyzing the theory of phase space reconstruction. G based on wavelet neural network is discussed by 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 from Lorenz simulation signal and rolling bearing vibration signal is used to verify the proposed method. It is found that the proposed wavelet neural network perform well in the chaotic time series prediction, and its results agree well with experimental data with high accuracy over BP network. This paper provides an effective approach with practical engineering significance to the prediction of nonlinear dynamic systems.

关 键 词：小波神经网络 混沌时间序列 相空间重构 

分 类 号：TP274.2[自动化与计算机技术—检测技术与自动化装置]