摘要
引入了一套磨粒形态学描述子来提取磨损颗粒的显微形态特征 ,然后以此为输入参数提出了一套BP神经网络 ,对磨损颗粒进行自动识别分类。针对本网络输入参数多 ,网络训练耗时长的问题 ,尝试采用因子模糊化的网络训练方法 ,大幅度提高了神经网络的训练速度 ,并取得了较好的应用效果。
A set of morphology descriptors of debris is presented to describe the micro feature of wear particles, and the program to auto identify wear particles by means of artificial neural network (ANN) technique is compiled. During training the network, the fuzzified factor based training technique given out in this paper is used, and the training process is accelerated rapidly. When the network is used to identify the wear particles, the identifying accuracy is higher than 90%, and the identifying speed is very fast. The method by far excels the traditional ones.
出处
《航空学报》
EI
CAS
CSCD
北大核心
2000年第4期372-375,共4页
Acta Aeronautica et Astronautica Sinica
关键词
双BP神经网络
因子模糊化
磨粒识别
double back propagation neural network
fuzzified factor
debris identification
