Combining blind source analysis and Elman recurrent neural network to determine overlapping voltammograms.
ICNC(2012)
摘要
A novel method named ICA-ERNN approach based on independent component analysis (ICA) as pre-processed tool with Elman recurrent neural network (ERNN) regression was proposed for the simultaneous differential pulse voltammetric determination of o-nitroaniline, m-nitroaniline and p-nitroaniline with overlapping peaks. The method combines the ideas of ICA with ERNN regression for enhancing the ability in the extraction of characteristic information and the quality of regression. A program (PICAERNN) was designed to perform the simultaneous voltammetric determination of o-nitroaniline, m-nitroaniline and p-nitroaniline. The relative standard errors of prediction (RSEP) obtained for all components using ICA-ERNN and ERNN were compared. Experimental results demonstrated that the ICA-ERNN method had better result than ERNN methods and was successful even when there was severe overlap of voltammgrams. © 2012 IEEE.
更多查看译文
关键词
blind source separation,elman recurrent neural network,multicomponent determination,nitroaniline isomers,overlapping voltammograms,recurrent neural network,artificial neural networks,matrix decomposition,independent component analysis,regression analysis,feature extraction,recurrent neural networks
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要