摘要
利用工件在加载与卸载时所产生的声发射信号,作为神经网络模式识别的输入特征,并对输出量进行模糊化隶属度计算,用BP学习法对连接权重系数进行优化,成功地实现了对工件样本组的训练和分类。
The acoustic emission signals produced when iron workpiece is loaded and unloaded are used as input
characteristics of pattern recognition for neural network. Meanwhile the fuzzy subordinate degree is calculated
for network output. The connection weight factors are optimized through BP learning. The training and classifi-
cation for workpiece sample group are successfully fulfilled.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2003年第z2期406-407,410,共3页
Chinese Journal of Scientific Instrument
关键词
声发射
神经网络
质量检测
Acoustic emission
Neural network
Quality evaluation
