%0 Journal Article
%T Prediction of the chaotic time series using support vector machines for fuzzy rule-based modeling<br>基于模糊模型支持向量机的混沌时间序列预测
%A Cui Wan-Zhao
%A Zhu Chang-Chun
%A Bao Wen-Xing
%A Liu Jun-Hua
%A <br>崔万照
%A 朱长纯
%A 保文星
%A 刘君华
%J 物理学报
%D 2005
%I 
%X Based on the powerful nonlinear mapping ability of support vector machines and the characteristics of fuzzy logic which can combine a prior knowledge into fuzzy rules, the forecasting model of the support vector machine for fuzzy rules-bas ed model in combination with Takens' delay coordinate phase reconstruction of ch aotic time series has been established; and the least squares method for large-s cale problems is used to train this model. Moreover, based on this model, relati onships among the prediction performances of this model, the embedding dimension and the delay time are discussed. Finally, the Mackey-Glass equation and the t ime series that Lorenz systems generate are applied to test this model, respecti vely, and the results show that the support vector machine for fuzzy rule-based modeling can not only acquire knowledge and generate fuzzy rules from the given data, reduce the number of support vectors greatly, but also predict chaotic ti me series accurately, and even if the embedding dimension is unknown and the del ay time is appropriately selected, the predicted results are satisfactory. These results imply the support vector machine for fuzzy rule-based modeling is a go od tool to study chaotic time series in practice.
%K fuzzy logic
%K chaotic time series
%K support vector machine
%K least squares method<br>模糊模型，
%K 混沌时间序列，
%K 支持向量机，
%K 最小二乘法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=29DF2CB55EF687E7EFA80DFD4B978260&aid=67CFEAEFFD144A08&yid=2DD7160C83D0ACED&vid=318E4CC20AED4940&iid=DF92D298D3FF1E6E&sid=36E66EEE6C9C1300&eid=D6A29273B6F10BE7&journal_id=1000-3290&journal_name=物理学报&referenced_num=5&reference_num=41