| 用BP神经网络法预测反相液相色谱保留值 |
| |
| 引用本文： | 徐晖,王乾宇,张干兵. 用BP神经网络法预测反相液相色谱保留值[J]. 湖北大学学报(自然科学版), 2002, 24(2): 145-148 |
| |
| 作者姓名： | 徐晖 王乾宇 张干兵 |
| |
| 作者单位： | 湖北大学化学与材料科学学院，湖北武汉430062 |
| |
| 摘 要： | 运用逐步回归法筛选BP人工神经网络的输入元素改进BP网络的结构，并用于疏水分配常数及氢键作用能预测反相液相色谱保留值，结果表明，逐步回归法与人工神经网络相结合，能优化网络，大大提高网络收敛速度在很大程度上克服了通常BP网络过所合的缺点，较通常BP神经网络及回归法在处理构效关系方面有更强的信息处理能力和预测能力。
|
| 关 键 词： | BP神经网络法 反相液相色谱 保留值 逐步回归 疏水分配常数 |
| 文章编号： | 1000-2375(2002)02-0145-04 |
| 修稿时间： | 2001-11-02 |
Prediction of retention indices in reversed-phase high-performance liquid chromatography with BP artificial neural networks |
| |
| Xu Hui,Wang Qianyu,Zhang Ganbing. Prediction of retention indices in reversed-phase high-performance liquid chromatography with BP artificial neural networks[J]. Journal of Hubei University(Natural Science Edition), 2002, 24(2): 145-148 |
| |
| Authors: | Xu Hui Wang Qianyu Zhang Ganbing |
| |
| Abstract: | The back-propagation (BP) neural network is improved by combining the multivariable stepwise regression and BP neural network,and used to predict the rentention indices in reversed-phase high-performance liquid chromatography with the partition coefficient and hydrogen bonding ability.The results show that the improved BP neural network has better performance such as accelerating the convergence and improving predictable ability than the original one and multivariable regression method. |
| |
| Keywords: | multivariable stepwise regression artificial neural networks partition coefficient the retention indices in reversed-phase high-performance liquid chromatography |
| 本文献已被 CNKI 维普 等数据库收录！ |
