摘要
在测控系统中遥测数据野点的存在给数据的进一步处理带来严重的困难,由于传统的野值剔除方法无法完全保留信号的高频信息,提出一种基于小波变换和矩分析理论的野值剔除新方法。该方法以原信号小波分解后的低频成分作为原信号的估计并对残差进行自适应野值剔除,将剔除野值后的信号重构,得到野值剔除后的理想信号。与传统方法相比,该方法既保留了信号的高频成分又有效剔除了野值。对固定门限和自适应门限两种野值剔除方法进行Matlab仿真比较,结果表明,自适应野值剔除方法对数据野值剔除具有很好的效果。
The presence of wild points in the measurement and control system,brings serious difficulties to the further processing of data.Since traditional methods cannot be completely exclude outliers and retain high frequency information signal,this paper presents a new method for excluding the outliers based on wavelet transform and moment theory.In this method,the original signal wavelet decomposition of low-frequency components as the original signal,estimates and removes outliers with adaptive method,reconstructs signal after removing outlier,then obtains ideal signal.Comparing with traditional methods,the method not only retains the high-frequency signal components but also effective exclude outliers.The fixed threshold and adaptive threshold method are compared by Matlab simulation,the results show that the adaptive method is better.
出处
《计算机与现代化》
2010年第8期11-14,共4页
Computer and Modernization
关键词
小波变换
野值剔除
自适应门限
wavelet transform
outliers removed
adaptive threshold
