摘要
用误差反向传播(BP)的人工神经网络(ANN)模型及分子结构描述码作为输入特征参数,预测气相色谱保留指数.研究了链烷烃、环脂烃、烯烃及醇、酯、醚等300个化合物,预测结果平均相对误差不大于2.83%.
A back-propagation neural network model has been trained to predict the gaschromatographic retention indices.A group of molecular descriptors which are proposed torepresent molecular branching,cyclization and unsaturation has been used as inputted struc-tural parameters.In 300 acyclic and cyclic alkanes,alkenes,alcohols,esters,ketones andethers studied,the mean relative error of the predicted results was less than 2.83%.
出处
《应用化学》
CAS
CSCD
北大核心
1994年第5期54-59,共6页
Chinese Journal of Applied Chemistry
关键词
神经网络
气相色谱
保留指数
as chromatography,gas chromatographic retention index,artificial neutral net-work,molecular descriptor