基于二代小波变换的信号去噪及其软测量建模

化工生产过程中采集到的数据信号通常具有随机性和非平稳性,附加了各种噪声,以至于影响数据建模的拟合效果和泛化性能.本文基于二代小波分析的特点,提出了一种对信号数据进行小波变换阈值去噪的方法.该方法可去除大部分高频随机噪声,提取真实信号,进而提高数据的置信度.将该方法与支持向量机相结合并应用于双酚A反应过程质量指标软测量模型中.仿真结果表明,该方法能有效恢复数据的真实性,提高数据建模的拟合精度与泛化性能.

Signal De-noising based on second generation wavelet transform and soft sensor modeling

Because of the nonstationarity and randomicity of data in the chemical production process,it contains different noises inevi- tably,which will affect the fitting and the generalization capability in data modeling.Based on the characteristics of wavelet analysis, this paper proposed a signal denoising method which combines the second generation wavelet transform with a new threshold function. The method can remove random noises,and extract true signals to improve the credibility of the data.Combined the method with sup- port vector machine and applied to a modeling for the performance figure of BPA productive process,the result indicates that it is effec- tive to regain the factuality of the data and improve the fitting and generalization capability of the soft sensor model.