%0 Journal Article
%T Multivariate chaotic time series phase space reconstruction based on extending dimension by conditional entropy<br>基于条件熵扩维的多变量混沌时间序列相空间重构
%A Zhang Chun-Tao
%A Ma Qian-Li
%A Peng Hong
%A Jiang You-Yi
%A <br>张春涛
%A 马千里
%A 彭宏
%A 姜友谊
%J 物理学报
%D 2011
%I 
%X For multivariate chaotic time series, a method of conditional entropy extending dimension(CEED) in the reconstructed phase space is proposed. First, the delay time of any variable time series is selected by mutual information method, and then the embedding dimension of phase space is extended by the conditional entropy. This method can ensure the independence of reconstructed coordinates from low space to high space and eliminate the redundancy of phase space, because the largest condition entropy is choosen. The effective input vector for the prediction of multivariate time series is given. Simulations of the Lorenz system and Henon system show that the neural network predictions of multivariate time series are much better than the prediction of univariate and existing multivariate. Therefore, CEED is effective for multivariate chaotic systems.
%K multivariate chaotic time series
%K phase space reconstruction
%K conditional entropy
%K neural network prediction<br>多变量混沌时间序列
%K 相空间重构
%K 条件熵
%K 神经网络预测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=29DF2CB55EF687E7EFA80DFD4B978260&aid=5A162B1B99E5B5E822BA3C3B14198AF1&yid=9377ED8094509821&vid=BFE7933E5EEA150D&iid=0B39A22176CE99FB&sid=E777309FD53E9128&eid=E777309FD53E9128&journal_id=1000-3290&journal_name=物理学报&referenced_num=0&reference_num=30