Learning and predicting time series by neural networks

被引：0
作者：
Freking, A [1 ]
Kinzel, W
Kanter, I
机构：
[1] Univ Wurzburg, Inst Theoret Phys & Astrophys, D-97074 Wurzburg, Germany
[2] Bar Ilan Univ, Minerva Ctr, IL-52900 Ramat Gan, Israel
[3] Bar Ilan Univ, Dept Phys, IL-52900 Ramat Gan, Israel
来源：
PHYSICAL REVIEW E | 2002年 / 65卷 / 05期
关键词：
D O I：
暂无
中图分类号：
O35 [流体力学]; O53 [等离子体物理学];
学科分类号：
070204 ; 080103 ; 080704 ;
摘要：
Artificial neural networks which are trained on a time series are supposed to achieve two abilities: first, to predict the series many time steps ahead and second, to learn the rule which has produced the series. It is shown that prediction and learning are not necessarily related to each other. Chaotic sequences can be learned but not predicted while quasiperiodic sequences can be well predicted but not learned.
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页数：4
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