Determining the input dimension of a neural network for nonlinear time series prediction

被引：0
|
作者：
Zhang, S [1 ]
Liu, HX
Gao, DT
Du, SD
机构：
[1] Nanjing Univ, Dept Elect Sci & Engn, Nanjing 210093, Peoples R China
[2] Nanjing Normal Univ, Dept Phys, Nanjing 210097, Peoples R China
来源：
CHINESE PHYSICS | 2003年 / 12卷 / 06期
关键词：
nonlinear time series; prediction; phase space reconstruction; neural network; input dimension;
D O I：
暂无
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
Determining the input dimension of a feed-forward neural network for nonlinear time series prediction plays an important role in the modelling. The paper first summarizes the current methods for determining the input dimension of the neural network. Then inspired by the fact that the correlation dimension of a nonlinear dynamic system is the most important feature of it, the paper presents a new idea that the input dimension of the neural network for nonlinear time series prediction can be taken as an integer just greater than or equal to the correlation dimension. Finally, some validation examples and results are given.
引用
收藏
页码：594 / 598
页数：5
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