期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Application of Back-propagation Artificial Neural Network in Speciation of Cadmium 被引量:1
1
作者 WANG Lin-lin ZHANG Jie +4 位作者 LIU Hai-yan ZHANG Hai-tao WANG Hong-yan YANG Xiu-rong WANG Ying-hua 《Chemical Research in Chinese Universities》 SCIE CAS CSCD 2010年第6期899-904,共6页
A method for predicting the five species contents of cadmium was developed by combining the back-propagation artificial neural network with graphite furnace atomic absorption spectrometry(BP-ANN-GF-AAS).Based on the... A method for predicting the five species contents of cadmium was developed by combining the back-propagation artificial neural network with graphite furnace atomic absorption spectrometry(BP-ANN-GF-AAS).Based on the strong learning function and the features of the information distributed storage of artificial neural network(ANN),a single ANN was constituted in which only one determination point of every sample was required.The exchangeable,carbonated,Fe-Mn oxidable,organic and residual species of cadmium for 20 kinds of soil samples from the two sections of Changchun(China) were determined by BP-ANN-GF-AAS.The detection limit of the method is 0.024 μg/L and the limit of quantification is 0.080 μg/L.t-Test indicates that there is not any systemic error of the results obtained by the Tessier sequential extraction graphite furnace atomic absorption spectrometry method(Tessier-GF-AAS) and BP-ANN-GF-AAS.Compared with those of the Tessier-GF-AAS,the prediction errors of BP-ANN-GF-AAS are less than 10%.The proposed method is fast,convenient,sensitive,and can eliminate the interference among various species. 展开更多
关键词 Artificial neural network(ANN) SPECIATION graphite furnace atomic absorption spectrometry(gf-aas CADMIUM
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部