改进的BP网络用于气相色谱保留指数预测  

Using Improved BP Neural Network in Predicting GC Retention Indices

作　　者：李睿[1] 高守国[1] 相秉仁[1] 

机构地区：[1]中国药科大学分析计算中心,南京210009

出　　处：《计算机与应用化学》2000年第1期113-114,共2页Computers and Applied Chemistry

基　　金：国家自然科学基金! ( 2 97750 33)资助项目

摘　　要：改进了BP神经网络的学习速率算法 ,并运用改进后的BP网络 ,通过分子结构参数 ,同时对烷基吡啶在非极性固定液SE 30和极性固定液PEG 1 50 0柱上的保留指数进行预测 ,取得了较好的结果。The back propagation neural network model is improved so that its learning rate can be automatically changed with the learning error. Based on the improved BP model, the relationship between the molecular parameters of a set of alkylpyridine and their GC retention indices observed on both SE 30 and PEG 1 500 columns is studied. The relative mean error (RME) of prediction is lower than 2%. The result also shows that the performance of improved BP model is better than the original one.

关 键 词：BP神经网络 保留指数 分子连接性指数 烷基吡啶 

分 类 号：O626.32[理学—有机化学] O561[理学—化学]