摘要
以分子拓扑指数作为炸药组分的结构描述符 ,利用反向传播算法 (BP)人工神经网络 ,以Sigmoid函数为传递函数 ,分子连接性指数0 χ ,1χ ,2 χ与边邻接指数 (ε)为输入向量 ,反相高效液相色谱保留值参数logkw 和S为输出向量 ,将输入向量归一化至 - 3～ 3区间 ,输出向量归一化至 0～ 1区间 ,网络精度取 0 5 ,学习步长 η的初始值取0 2 ,动量因子α取 0 5 ,通过对 2 0种炸药的网络模型进行训练 ,建立了炸药分子结构与logkw 和S之间的定量模型。结果表明 ,该模型较好地反映了炸药分子结构与保留值之间的关系 ,预测值与文献所报道实验值的相对误差大部分在± 5 %内 。
The quantitative relationship between the retention parameters and the structure of explosives is discussed Molecular topological indices are used to represent the structure Based on the back propagation algorithm, a quantitative model was established after a training process of a train set containing 20 explosives being completed The Sigmoid function was chosen as the transmit function The retention parameters (log k w and S ) acted as output vectors, while molecular connecting indices ( 0χ, 1χ, 2χ) and edge adjacent indices( ε ) acted as input vectors The input vectors were normalized in the range of -3 3 and the output vectors were normalized in the range of 0 1 The accuracy of network was 0 5 and the beginning value of studying pace ( η ) was 0 2, the momentum factor ( α ) was 0 5 The results showed that the yield model reflected the relationship between the structure and retention index of compounds, and had high accuracy Most of the relative errors were below ±5%
出处
《色谱》
CAS
CSCD
北大核心
2001年第4期319-322,共4页
Chinese Journal of Chromatography
