摘要
对于随机突变参量的数据可靠性处理,本文提出了一种新的基于自适应预报的数据处理器算法.该算法将被测参量的变化规律用一参数慢时变的时间序列模型描述,以被测参量变化趋势的自适应一步预报值及其95%置信限构成判据,对测量数据进行处理,剔除或抑制其中的不良数据.仿真实例表明本算法克服了现有大多数算法在处理随机突变数据时遇到的困难,效果明显.
In regard to stochastic and suddenly varying data,a new kind of data processor algorithm based on adaptive prediction is presented.The algorithm describes the changing pattern of measured variables by means of time series models with slowly time-varying parameters,forms the criterion by use of the adaptive and its 95% belief
limit,and processes the measured data so as to reject or restrain outliers mixed in them.The simulation example indicates that the algorithm surmounts difficulties which most current algorithms encounter when processing stochastic and suddenly varying data and that it is of obvious effects.
关键词
随机变量
数据处理
可靠性
data-processing
stochastic variable
time seriesanalysis
adaptive prediction
