基于自适应学习算法的小波消噪在热工过程信号处理中的应用  

APPLICATION OF WAVELET DE-NOISING IN SIGNAL PROCESSION OF THERMAL ENGINEERING PRCESS

作　　者：杨振勇[1] 尹喜超[2] 

机构地区：[1]华北电力科学研究院有限责任公司,北京100045 [2]北京市电力公司,北京100045

出　　处：《热力发电》2008年第12期73-78,共6页Thermal Power Generation

摘　　要：小波变换具有局部性特点,其在信号降噪方面有较强优势。但是,在消噪过程中阈值的选取将直接影响到信号处理效果,而软阈值和硬阈值消噪方法都有不足之处。对此,引入了梯度的自适应学习算法以求取最佳阈值,并将该方法用于处理热工过程信号,为故障诊断和系统辨识提供更接近实际的信号。The wavelet transform has partial features, and boasting stronger advantage in signal de - noising. However, the threshold value choise in de- noising process will directly affect the result of signal processing, and both of soft and hard threshold value de - noising methods have their deficiencies. For this,an adaptive learning algorithm of gradient has been introduced for seeking the optimal threshold value, and the said method being applied in signal processing of thermal Engineering process, providing signal more nearing to the actual one for fault diagnosis and system identification.

关 键 词：自适应 热工信号 小波分析 消噪 值 

分 类 号：TP202.7[自动化与计算机技术—检测技术与自动化装置]