| 陈彦华,李明轩.利用人工神经网络实现缺陷类型识别[J].,1998,17(2):1-4,10 |
| 利用人工神经网络实现缺陷类型识别 |
| Classification of flaws through an artificial neural network |
| |
| 中文摘要: |
| 本文在对各向同性均匀固体中横穿孔、平底孔和裂缝缺陷超声散射特性分析的基础上，分别用回波幅度话和去卷积幅度措作为特征量，利用人工神经网络对缺陷类型进行识别．结果表明，用去卷积幅度谱作为特征量时，利用人工神经网络对这三类缺陷的类型识别，可获得较理想的结果． |
| 英文摘要: |
| In this paper, the scattering characteristics of an ultrasonic wave in a homegeneous solid by three types of flaw are considered the three types being the traversecylindrical cavily, the blat-bottom hole and the plane crack. The flaws are then classifiedby a neural network on using the respectively amplitude spectra of ultrasonic echoes andthose of deconvolved ultrasonic echoes as characteristic features. It is demonstrated thatthe latter improve greatly the classification accuracy. |
| DOI：10.11684/j.issn.1000-310X.1998.02.001 |
| 中文关键词: 人工神经网络 缺陷类型识别 去卷积 |
| 英文关键词: Artifical neural network Classifcation of flaws Deconvolution |
| 基金项目:国家自然科学基金;中国科学院声学研究所资助 |
|
| 摘要点击次数: 3999 |
| 全文下载次数: 922 |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
