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《Acta Scicentiarum Naturalum Universitis Pekinesis》 2001-02
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Combined Wavelet Transform with Neural Network for Oscillographic Chronopotentiometric Determination

ZHENG Jianbin 1) ZHANG Jun 1) ZHONG Hongbo 2,3) LIU Hui 1) LI Guanbin 2) CHEN Liren 3) ( 1) Institute of Electroanalytical Chemistry,Northwest University,Xi'an,710069; 2) Department of Che  
Combination of wavelet transform with function of data compression and neural network is firstly applied to predict quantitatively the concentration of Pb 2+ and other ions in oscillographic chronopotentiometric determination(OCPD).Compared with RPNN and WNN,combination of the wavelet transform and neural network(CWTNN)for OCPD has higher prediction accuracy and less convergence epoch.This can be explained from two aspects.Firstly,the network operation rate has been greatly enhanced because the optimal detail signal obtained after the wavelet transform not only is the characteristic information in original signal,but also has less number of data points than that of original signal.Secondly,the higher prediction accuracy can be obtained because detail signal used as network input contains lower noise.As a result,the potential application of CWTNN for OCPD and other electroanalytical methods will be very excellent.
【CateGory Index】: O657.1
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