%0 Journal Article
%T Parameter joint estimation of phase space reconstruction in chaotic time series based on radial basis function neural networks<br>基于径向基函数神经网络的混沌时间序列相空间重构双参数联合估计
%A Chen Di-Yi
%A Liu Ye
%A Ma Xiao-Yi
%A <br>陈帝伊
%A 柳烨
%A 马孝义
%J 物理学报
%D 2012
%I 
%X In this paper, we propose a joint estimation method of two parameters for phase space reconstruction in chaotic time series, based on radial basis function (RBF) neural networks. And we obtain the best estimation values, according to some objective standards. Furthermore, The single-step and multi-step RBF prediction model is used to estimate the best embedding dimension and delay time, and Lorenz system is selected as an example. Finally, the estimation values are tested in the original model. The simulations show that we can obtain the best estimation values through the method, and the prediction accuracy is significantly improved.
%K phase space reconstruction
%K radial basis function neural networks
%K parameter estimation
%K prediction<br>相空间重构
%K 径向基函数神经网络
%K 参数估计
%K 预测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=29DF2CB55EF687E7EFA80DFD4B978260&aid=78F360FC29C47911C8E5C0D2DAF32D77&yid=99E9153A83D4CB11&vid=1D0FA33DA02ABACD&iid=F3090AE9B60B7ED1&sid=791033C15C1F5CB8&eid=791033C15C1F5CB8&journal_id=1000-3290&journal_name=物理学报&referenced_num=0&reference_num=26