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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shouxin Ren Ling Gao |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Chem., Inner Mongolia Univ., Huhhot (Shouxin Ren; Ling Gao) |
| Abstract | A novel method named WPTRBF is presented in this paper. This method is based on radial basis function neural network (RBFN) with wavelet packet transform (WPT) as a pre-processing tool for the simultaneous differential pulse stripping voltammetric determination of Pb(II), Ni(II) and Cd(II). Wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of noise removal can be improved. Radial basis function network was applied for overcoming the convergence problem met in back propagation training and for facilitating nonlinear calculation. In this case, through optimization, the number of wavelet function, decomposition level, the number of hidden nodes and the width (sigma) of RBFN for the WPTRBFN method were selected as Daubechies 4, 3, 8 and 0.7 respectively. The relative standard errors of prediction for all elements with WPTRBFN and RBFN were 5.8% and 6.89% respectively. |
| Starting Page | 102 |
| Ending Page | 105 |
| File Size | 328420 |
| Page Count | 4 |
| File Format | |
| ISBN | 9780769534848 |
| DOI | 10.1109/WMSO.2008.49 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-12-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Wavelet transforms Simultaneous differential pulse stripping voltammetric determination Wavelet domain Optimization methods Artificial neural networks Electronic mail Data mining Convergence Chemistry Radial basis function networks Wavelet packet transform Wavelet packets Radial basis function network |
| Content Type | Text |
| Resource Type | Article |
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