【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。更多还原

【Abstract】 By means of artificial neural network and Levenberg-Marquardt back-propagation train algorithm, the three-component metal 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.更多还原