摘要
由K、Na、Ca、Mg、Fe、Cu、Zn和Mn在老年性晶状体中的含量,运用人工神经网法成功地将老年性白内障、白内障晶状体核和正常人晶状体划分为3类。同时讨论了神经网的结构(层数及每层的结点数)、初始权重等对神经网性能的影响。随机地将30个晶状体分为训练集和测试集,其识别率及预测率均达到100%。
Senile cataract lenses.nuclei from cataract lenses,and normal lenses were successfully separated into three classes using quasi-Newton neural networks. Tlie lenses were classified based on the concentrations of K,Na,Ca,Mg,Cu,Fe,Zn and Mn measured by atomic absorption spectroscopy.The 30 cataract lenses used in this study were randomly divided into a 23 member training set and 7 member test set.The architecture including the number of layers,the number of neurons in each layer,and the initial weights were varied in order to study their effects on the performance of the neural network.Once trained,the neural network correctly classified all 30 cataract lenses.
出处
《高等学校化学学报》
SCIE
EI
CAS
CSCD
北大核心
1994年第7期982-985,共4页
Chemical Journal of Chinese Universities
关键词
神经网
金属元素
白内障
晶状体
Neural network,Human senile cataract,Trace metal elements