人工神经网络用于光度法同时测定铜钴镍  

Application of Artificial Neural Network to Simultaneous Spectrophotometric Determination of Cu, Co and Ni

作　　者：何池洋[1] 孙益民[2] 吴根华[1] 陈荣[3] 

机构地区：[1]安徽师范大学化学与材料科学学院,安徽芜湖241000 [2]安庆师范学院化学系,安徽安庆246011 [3]北京大学化学与分子工程学院,北京100871

出　　处：《光谱学与光谱分析》2001年第5期719-722,共4页Spectroscopy and Spectral Analysis

基　　金：安徽省自然科学基金 (No 0 0 0 4 650 9)资助项目

摘　　要：本文采用PAR Cu ,Co ,Ni显色体系 ,应用人工神经网络原理 ,以Levenberg MarquardtBP算法 ,对紫外吸收光谱严重重叠的三组分金属配合物体系同时进行含量测定。在 4 5 2～ 5 5 2nm的范围内 ,以 14个特征波长处的吸收值作为网络特征参数 ,并通过正交设计安排样本。网络仅训练 781次即可达到要求 ,Cu ,Co ,Ni三者的平均回收率分别为 99 99% ,99 96% ,99 97% ,测定结果的相对标准偏差分别为 0 1% ,0 2 % ,0 1%。实验表明 ,该方法与现有的算法相比具有训练速度快、预测结果准确度高等特点。该方法和光度法结合有望成为多组分分析的有效方法之一。By means of artificial neural network and Levenberg-Marquardt back-propagation train algorithm, the three-component met al coordinate compounds of PAR-Cu, Co, Ni were determined simultaneously, in which the spectra overlapped. In 452-552 nm, the absorbance(A) at 14 wavelength were taken as character of artificial neural network, and samples were arranged by method of orthogonal design. The mean recovery of Cu, Co, Ni were 99.96%, 99.99% and 99.97% respectively. The RSD of the results were 0.1%, 0.2% and 0.1% respectively. The results were better than those of other networks in training speed and the accuracy. In conclusion the new network spectrophotometry is a good choice for resolving multicomponent.

关 键 词：人工神经网络 光度法 铜 钴 镍 同时测定 多组分分析 

分 类 号：O657.32[理学—分析化学] TP183[理学—化学]