小波神经网络在超声检测信号降噪中的应用  

Application of Wavelet Neural Network in Signal Denoising of Ultrasonic Detection

作　　者：鲁道邦[1] 宋玉玲[1,2] 程建政[3] 

机构地区：[1]南阳师范学院物理与电子工程学院,河南南阳473061 [2]陕西师范大学物理与信息技术学院,西安710062 [3]武汉纺织大学电子信息工程学院,武汉430071

出　　处：《噪声与振动控制》2010年第5期145-148,共4页Noise and Vibration Control

基　　金：国家自然科学基金(10774164);南阳师范学院青年基金(QN2009023)

摘　　要：为了最大限度地消除粗晶材料超声检测时,晶粒散射波对有用信号的严重干扰,提高接收信号的信噪比,将小波神经网络引入粗晶材料超声检测信号处理领域中。在训练小波神经网络时,采用了改进的梯度下降算法。该网络有一个动态的权值,它随误差变化而调整。结果表明,小波神经网络应用在粗晶材料超声检测信号的降噪时,能够达到较理想的降噪效果。In order to minimize the disturbance of the scattering wave induced by crystal-grain-structure of the material to the useful signals and raise the signal-to-noise ratio（SNR）,wavelet neural network is introduced to the signal denoising for ultrasonic detection of coarse-grain materials.The improved gradient descent algorithm has been used to train the wavelet neural network.In the process of training,the network has a dynamic weight,which can be updated automatically according to the gradient descent with adaptive learning rate.The experimental results show that wavelet neural network is an effective method in signal denoising of ultrasonic detection.

关 键 词：声学 降噪 小波神经网络 粗晶材料 超声检测 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]