[Simultaneous resolution of overlapped spectra of three kinds of organic compounds using a wavelet packet transform-based generalized regression neural network].

A wavelet packet transform-based generalized regression neural network (WPTGRNN) was developed to perform simultaneous spectrophotometric determination of p-nitroaniline, alpha-naphthylamine and benzidine. This method combines wavelet packet transform (WPT) with generalized regression neural network (GRNN) for improving the quality of noise removal and enhancing the ability of prediction. Wavelet packet representations of signals provided a local time-frequency description and separation ability between information and noise.

[Studies on simultaneous quantitative determination of overlapping spectra using chemometric techniques].

Based on comparative study of eight chemometric denoising methods, a wavelet packet transform Elman recurrent neural network (WPERNN) method was developed to study simultaneous quantitative determination of overlapping spectra. The quality of noise removal and ability of regression were improved by combining wavelet packet transform with Elman recurrent neural network. Through optimization, the wavelet function, the wavelet packet decomposition levels as well as the structure and parameters of Elman recurrent neural network were selected.

[Simultaneous determination of Sm( III) and Y(III) by spectrophotometry with a wavelet packet transform latent variable regression].

A wavelet packet transform latent variable regression (WPLVR) method was developed to perform simultaneous quantitative analysis of Sm(III) and Y(III). The quality of the noise removal was improved by combining wavelet packet transform with latent variable regression (VLR). Wavelet packet representations of signals provided a local time-frequency description, thus in the wavelet domain, the quality of the noise removal can be improved.

[Simultaneous quantitative analysis of three kinds of phenols by Elman recurrent neural network].

Elman recurrent neural network ( ERNN) was applied to study the simultaneous quantitative analysis of seriously overlapped spectra of a p-nitrophenol, o-nitrophenol and 2,4-dinitrophenol system. The multivariate linear regression (MLR) method was also applied in this study for comparison. Two programs (PERNN and PMLR) were designed to perform the calculations.

[Simultaneous spectrophotometric determination of multicomponent mixtures by a soft thresholding wavelet-based radial basis function neural network].

A Soft Thresholding Wavelet-based Radial Basis Function Neural network (STWRBFN) method was developed to perform simultaneous quantitative analysis of multicomponent mixtures. The quality of noise removal and regression was improved by combining wavelet soft thresholding with radial basis function neural network. Through optimization, the wavelet function, wavelet decomposition level (L), thresholding method and spread parameter sigma of RBFN were selected.