%0 Journal Article
%T Prediction of multivariable chaotic time series using optimized extreme learning machine<br>采用优化极限学习机的多变量混沌时间序列预测
%A Gao Guang-Yong
%A Jiang Guo-Ping
%A <br>高光勇
%A 蒋国平
%J 物理学报
%D 2012
%I 
%X A prediction algorithm of multivariable chaotic time series is proposed based on optimized extreme learning machine (ELM). In this algorithm, a presented composite chaos system and mutative scale chaos method are utilized first to search and optimize the parameters of ELM for improving the generalization performance. Then the optimized ELM is used to predict the multivariable chaotic time series of Rossler coupled system for single step and muti-step, and the scheme is compared with the congeneric method, which shows the validity and stronger ability against noise of the developed algorithm. Finally, the relation between prediction result and number of hidden neurons is discussed.
%K extreme learning machine
%K multivariable chaotic time series
%K prediction of chaotic time series
%K composite chaos optimization<br>极限学习机
%K 多变量时间序列
%K 混沌序列预测
%K 复合混沌优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=29DF2CB55EF687E7EFA80DFD4B978260&aid=2C2DE33E892A8820013F6E3917750093&yid=99E9153A83D4CB11&vid=1D0FA33DA02ABACD&iid=E158A972A605785F&sid=459AA7916576ACBA&eid=459AA7916576ACBA&journal_id=1000-3290&journal_name=物理学报&referenced_num=0&reference_num=17