人工神经网络-分光光度法同时测定硝基苯类化合物

Simultaneous Determination of Multicomponent Nitrobenzene Compounds by Artificial Neural Networks and Visible Spectroscopy

用人工神经网络一分光光度法同时测定硝基苯和对硝基氯苯，着重考察了２，４－二硝基氯苯、对硝基甲苯和间硝基甲苯等干扰物共存及染料、制药等工业废水基体对测定的影响．研究表明，用多层ＢＰ网常规训练法处理合成样品，结果理想．而对废水基体测定时误差较大，但采用本文提出的“有偏训练法”可明显提高测定的准确度．

Simultaneous determination of nitrobenzene and p-chloronitrobenzene were studied by artificial neural networks and visible spectroscopy in the paper. The effects of 2,4- dinitrochloro-benzene, p-nitrotoluene, m-nitrotoluene and the industrial wastewater matrix from the production of dye and pharmacy on the determination were mainly studied. The research indicated that when the common training method of BP algorithm was applied to the treatment of composed samples, the result was satisfactory. However, when it was applied to the determination of wastewater matrix, the error was large. But the biased training method proposed in this paper could improve the determination accuracy obviously.