基于“松散型”小波神经网络的时间序列预测研究

分析了一种新的时间序列预测模型——“松散型”小波神经网络预测模型。在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。运用该模型对太阳黑子年平均序列进行小波分解、重构、预测和合成，得到了序列总的预测效果。同时，将新模型与传统BP神经网络模型的预测效果进行了比较，分析了两者出现差异的本质原因。整体反映了将复杂问题简单化处理、将小波多分辨分析同神经网络的非线性逼近功能相结合的思想。这种思想及方法发挥了小波变换和神经网络的各自优势，明显提高了预测精度。

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

In this paper,a new time-serial prediction model called relaxing wavelet neural-net is analyzed.At the same time,it decides the length of sample serial by using a kind of statistic method called variance analysis so that the knot number of input layer can be efficiently decided,which is based on analyzing time-serial prediction by means of neural-net.Using this model,it operates the annual average sunspot serial through wavelet-decomposing,reconstructing,prediction and composing.Then,the total prediction effect is gotten.At the same time,the prediction effect about new model and traditional BP-net is compared and the substaintial cause of prediction difference of both types is analyzed.In total,this paper reflects a kind of thought which simplifies complex problem and combines wavelet’s multi-discrimination with neural-net’s non-linear approaching function.This idea exerted wavelet-transformation and neural-net’s advantage respectively.Thus,the prediction precision is obviously enhanced.