Multicomponent Kinetic Determination by Wavelet Packet Transform Based Elman Recurrent Neural Network Method

查看全文 作　　者：RENShou-xin [1]GAOLing

机构地区：[1]CollegeofChemistryandChemicalEngineering,InnerMongoliaUniversity,Huhhot010021,P.R.China

出　　处：《Chemical Research in Chinese Universities》索引2004年第20卷第6期,共5页

基　　金：National Natural Science Foundation of China(No.2 996 5 0 0 1) and Natural Science Foundation of InnerMongolia(No.2 0 0 2 2 0 80 2 0 115 ) 摘　　要：This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. The Elman recurrent network was applied to non-linear multivariate calibration. In this case, by means of optimization, the wavelet function, decomposition level and number of hidden nodes for WPTERNN method were selected as D4, 5 and 5 respectively. A program PWPTERNN was designed to perform multicomponent kinetic determination. The relative standard error of prediction(RSEP) for all the components with WPTERNN, Elman RNN and PLS were 3.23%, 11.8% and 10.9% respectively. The experimental results show that the method is better than the others. 关键词：多组分动力学微波束转换 Elman循环神经网络高碘酸盐碘酸盐

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Multicomponent Kinetic Determination by Wavelet Packet Transform Based Elman Recurrent Neural Network Method

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