摘要
本文将迭代目标转换因子分析与人工神经网络法用于分光光度法同时测定邻、间、对硝基甲苯,并与目标转换因子分析的结果进行了比较.结果表明,迭代目标转换因子分析法与线性网络法的效果都很好.其相对误差分别为1.3％和1.2％,而目标转换因子分析法的预测误差较大,其相对误差为10.4％.
Iterative target transformation factor analysis (ITTFA) and artificial neural network (ANN) are used to determine o-, m-, p-nitromethylbenzene simultaneously with spec-trophotometry. After compared the results obtained from these two methods above mentioned with those from target transformation factor analysis, it shows that satisfied prediction precision could be obtained by ITTFA and ANN methods. The average relative errors of ITTFA and ANN are 1. 3% and 1. 2% respectively while target transformation factor analysis is 10. 4%. It also shows that the satisfied results can be obtained by artificial neural network when 3 layers network (L=3) and 10 neurons in each hidden layer(N=10) are adopted.
出处
《分析化学》
SCIE
EI
CAS
CSCD
北大核心
1995年第10期1172-1175,共4页
Chinese Journal of Analytical Chemistry
基金
国家自然科学基金资助项目
关键词
因子分析法
神经网络法
分光光度法
硝基甲苯
Iterative target transformation factor analysis, artificial neural network algorithm, spectrophotometry, o-, p-, m-nitromethylbenzene
