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This paper presented a novel method named OSCWPTRBFN based on the concept of data mining in chemometrics for resolving overlapping spectra. The method combines orthogonal signal correction, wavelet packet transform and radial basis function neural network for enhancing the ability of removing noise and eliminating unrelated information as well as improving the quality of the regression method. OSC...
A novel method named WPTRBFN is presented in this paper. This method is based on radial basis function neural network (RBFN) with direct orthogonal signal correction (DOSC) and 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). DOSC was applied to remove structured noise that is unrelated...
This paper suggests a novel method named DOSCWTRBFN based on radial basis function neural network (RBFN) with direct orthogonal signal correction (DOSC) and wavelet transform (WT) as a pre-processing tool for the simultaneous spectrophotometric determination of Mn(II), Zn(II), Co(II) and Cd(II). In this case, by optimization, the number of DOSC components, tolerance factor, wavelet function, decomposition...
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...
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