%0 Journal Article
%T FAST EVOLVING MULTI-LAYER PERCEPTRONS FOR NOISY CHAOTIC TIME SERIES MODELING AND PREDICTIONS<br>
%A Zhang Jia-shu
%A Xiao Xian-ci
%A <br>
%J 中国物理 B
%D 2000
%I 
%X A fast evolutionary programming (FEP) is proposed to train multi-layer perceptrons (MLP) for noisy chaotic time series modeling and predictions. This FEP, which uses a Cauchy mutation operator that results in a significantly faster convergence to the optimal solution, can help MLP to escape from local minima. A comparison against back-propagation-trained networks was performed. Numerical experimental results show that the FEP can help MLP better capturing dynamics from noisy chaotic time series than the back-propagation algorithm and produce a more consistently modeling and prediction.
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=CD8D6A6897B9334F09D8D1648C376FB4&aid=D7B2071F525CBF4710553C85DCE09384&yid=9806D0D4EAA9BED3&vid=9CF7A0430CBB2DFD&iid=B31275AF3241DB2D&sid=A53D7AA35F9929AF&eid=09AA1448D1EAF9C1&journal_id=1009-1963&journal_name=中国物理&referenced_num=2&reference_num=0