基于小波重构高频采样数据方法的研究

Research of High Frequency Sampling Data Reconstruction Methods Based on Wavelet Theory

在工业过程中许多变量演化在不同的时间尺度上,而实际应用又往往要求采样数据在时序上能够匹配。满足这一要求的一般方法是从低频采样数据恢复高频采样数据。文章首先分析了基于小波变换的重构高频采样数据方法的收敛性及其局限,然后提出了一种基于小波网络的数据重构方法并进行了算法稳定性分析。仿真研究结果表明该方法是有效的和可行的。

Many variables often respond over different time scales in industrial processes,and for actual application data need to match each other in time sequence.Common method is to reconstruct higher frequency sampling data from low-er frequency sampling data.At first,the convergence and limitations of reconstructing higher frequency sampling data based on wavelet transformation are analyzed;then an algorithm for data reconstruction and its stability are studied;fi-nally,Simulating results are given to illustrate that the method is effective and feasible.