摘要
内层表面细小凹坑的识别是扭声无损评估的一个难点．本文利用人工神经网络对于信号的分类功能，建构和训练了一个ＢＰ神经网络，并用它对尺寸为１ｍｍ的圆锥形和半球形两种凹坑成功地进行了识别．研究表明，应用凹坑回波的ＤＣＴ 谱作为缺陷特征输入，可使ＢＰ神经网络的训练和缺陷识别既快捷又有效．
It is usually difficult to identify the tiny pits on an internal surface by ul trasonic nondestructive evaluation. Based on the classification ability of the artificial neural networks, a BP neural networks has been built, by which two kinds of pits are successfully identified. The research shows that using the DCT spectrum as the input of the networks enables the BP neural networks quick and effective in its training and identification.
出处
《应用声学》
CSCD
北大核心
2001年第5期12-15,共4页
Journal of Applied Acoustics
