摘要
非线性时间序列分析是目前迅速发展的一个课题，这是因为在现实世界中许多现象都不能很好地用线性模型解决。文章首先分析了时间序列模型的建立机制，然后利用神经网络进行非线性信号处理，从而构造了一种新的神经网络非线性时间序列模型。该文将此方法与ＡＲ模型和ＳＥＴＡＲ模型进行了数值结果对比，结果表明该文提出的方法优于这两种方法。
The nonlinear time series method is developing rapidly now, because many phenomena inthe real world can not be solved by the linear model.This paper first analyzes structure-mechanism about the time series model, and then aneural netwok is used to deal with the nonlinear signal process. We have structured a new non-linear time series neural network model.We have also compared the method with the AR model and the SETAR model. Using thethree methods , we have forceasted the number of sunspots and the results show that the neu-ral network method is better than the two others.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
1994年第1期73-78,共6页
Journal of Xidian University
基金
中科院管理
决策与信息系统开放实验室资助
关键词
非线性时间
神经网络
时间序列
nonlinear time series
neural network
forecastion
