%0 Journal Article
%T Chaotic time series prediction based on multi-kernel learning support vector regression<br>基于多重核学习支持向量回归的混沌时间序列预测
%A Zhang Jun-Feng
%A Hu Shou-Song
%A <br>张军峰
%A 胡寿松
%J 物理学报
%D 2008
%I 
%X Multi-kernel learning support vector regression (MKL-SVR) are proposed for chaotic time series prediction to solve the problems of kernel selection and hyper-parameter determination when using the standard SVR. The algorithm is realized through quadratic constrained quadratic programming (QCQP) in the hybrid kernel space, which not only reduces the number of support vectors, but also improves the prediction performance. Finally, it is applied to Mackey-Glass, Lorenz and Henon chaotic time series prediction. The results indicate that the proposed method can effectively increase the prediction precision, accelerate the convergency of cascade learning and enhance the generalization of prediction model.
%K chaotic time series
%K support vector machines
%K multi-kernel learning
%K optimization<br>混沌时间序列
%K 支持向量机
%K 多重核学习
%K 优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=29DF2CB55EF687E7EFA80DFD4B978260&aid=0A802925CFD680338E272AAA53DDA3C3&yid=67289AFF6305E306&vid=11B4E5CC8CDD3201&iid=94C357A881DFC066&sid=C244C42120BE39BF&eid=37CFEB51C5BA7883&journal_id=1000-3290&journal_name=物理学报&referenced_num=0&reference_num=16