摘要
建立了基于免疫遗传算法的人工神经网络结构,用于预测α-和β-环糊精与单取代或1,4-二取代苯衍生物包结物的稳定常数,其中神经网络用于建立取代苯中2个取代基的摩尔折射率R_m,疏水性参数π和Hammett常数σ与包结物稳定常数之间的QSAR模型,而免疫遗传算法则用于优化网络的权重系数。结果表明,由于免疫机制的引入使遗传算法的优化效率提高,神经网络的学习功能得到明显改善。
An artificial neural network based on an immune genetic algorithm was established to predict the association constants for the inclusion complexation of α- and β-cyclodextrin with mono- or 1, 4-disubstituted benzenes. The neural network was used to construct the QSAR model of the association constants with the substituent molar refraction Rm, hydrophobic constant ir, and Hammett constant σ of the substituents in benzene derivatives, and the immune genetic algorithm was employed to optimize the weights in th...
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2002年第5期546-550,共5页
Computers and Applied Chemistry
基金
国家自然科学基金(29975027
20172048)
关键词
免疫遗传算法
人工神经网络
环糊精
稳定常数
immune genetic algorithms
artiiicial neural network
cyclodextrin
association constant
