工程测量数据中的粗差诊断和处理技术

TECHNOLOGY OF DIAGNOSIS AND ADJUSTMENT FOR OUTLIERS IN MEASURED DATA OF ENGINEERING

从测量数据序列的时-频特征出发，借助频谱图识别测量数据中的粗差的位置和性质。采用加权的均方误差准则来优化估计模型的参数，实现对测量序列的抗扰最佳估计。实例表明利用频谱图进行粗差诊断准确可靠，采用加权误差能量函数的小波神经网络估计模型具有逼近性能好、收敛速度快的优点，并能够有效地消除粗差对估计结果的影响。

An approach based on time-frequency analysis to recognize outliers in measured data series by spectrogram is proposed, which discovers the time-frequency character of measured data series. To obtain the best estimation of anti-jamming for measured series, the rule of weighted-square error is introduced to optimize the parameters of estimating model. Example shows that diagnosing outliers by spectrogram is accurate and reliable, and wavelet neural network of weighted error-energy function has excellent approximation ability and fast convergence speed. Moreover, the outliers' influence on the estimate result can be eliminated efficiently by this way.