Jouyban A, Majidi M R, Altria K D, Clark B J, Asadpour-Zeynali K
School of Pharmacy, Tabriz University of Medical Sciences, Iran.
Pharmazie. 2005 Sep;60(9):656-60.
An artificial neural network (ANN) methodology was used to model the electrophoretic mobility of basic analytes in binary solvent electrolyte systems. The electrophoretic mobilities in pure solvent electrolytes, and the volume fractions of the solvents in mixtures were used as input. The electrophoretic mobilities in mixed solvent buffers were employed as the output of the network. The optimized topology of the network was 3-3-1. 32 experimental mobility data sets collected from the literature were employed to test the correlation ability and prediction capability of the proposed method. The mean percentage deviation (MPD) between the experimental and calculated values was used as an accuracy criterion. The MPDs obtained for different numerical analyses varied between 0.21% and 13.74%. The results were also compared with similar calculated mobilities which were derived from the best multiple linear model from the literature. From these results it was found that the ANN methodology is superior to the multiple linear model.
采用人工神经网络（ANN）方法对二元溶剂电解质体系中碱性分析物的电泳迁移率进行建模。以纯溶剂电解质中的电泳迁移率以及混合物中溶剂的体积分数作为输入。混合溶剂缓冲液中的电泳迁移率用作网络的输出。网络的优化拓扑结构为3-3-1。利用从文献中收集的32个实验迁移率数据集来测试所提出方法的相关性和预测能力。实验值与计算值之间的平均百分比偏差（MPD）用作准确性标准。不同数值分析得到的MPD在0.21%至13.74%之间变化。还将结果与从文献中最佳多元线性模型得出的类似计算迁移率进行了比较。从这些结果发现，人工神经网络方法优于多元线性模型。
