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基于“紧致型”小波神经网络的时间序列预测研究罗 航 王厚军 龙 兵


  摘 要:分析了“紧致型”小波神经网络的结构和特点。在利用神经网络分析时间序列预测方法的基础上,用方差分析的统计方法确定样本序列的长度,从而有效地确定神经网络输入层节点数。对太阳黑子年平均活动序列进行了训练和预测,并从网络本身的内在制约因素出发比较了小波网络和BP网络对时间序列进行训练和预测的差异,分析了两者出现差异的本质原因。将多分辨率的小波与神经网络的非线性逼近功能相结合的方法发挥了各自优势,明显提高了预测精度。

  关键词:小波神经网络; 方差分析; 时间序列; 预测

  中图分类号:TP274 文献标志码:A 文章编号:1001-3695(2008)08-2366-03

  Research about time-serial prediction based on tight wavelet neural-net

  LUO Hang, WANG Hou-jun, LONG Bing

  (School of Automation Engineering, University of Electronic Science & Technology of China, Chengdu 610054, China)

  Abstract:This paper analyzed the characteristic and construction of wavelet neural-net called tight type. At the same time, it decided the length of sample serial by using a kind of statistic method called variance analysis so that the knot number of input layer could be efficiently decided, which was based on analyzing time-serial prediction by means of neural net. It trained and predicted the time-serial of sunspot’s annual average activity and compared the difference of training and prediction to the time-serial between wavelet-net and BP-net from net itself restriction. Accordingly, analyzed the cause of that difference. The method of combining wavelet’s multi-discrimination with neural-net’s non-linear approaching function exerted their advantages respectively. Thus, the prediction precision was obviously enhanced.

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