人工神经网络法预测炸药组分的色谱保留值参数

 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。

Prediction of Retention Parameters of Explosivesby Artificial Neural Network

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 kappa w and S) acted as output vectors, while molecular connecting indices (0 chi, 1 chi, 2 chi) and edge adjacent indices(epsilon) 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 (eta) was 0.2, the momentum factor (alpha) 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%.