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A novel method, referred to as OSC-WPT-PLS approach based on partial least squares(PLS) regression with orthogonal signal correction(OSC) and wavelet packet transform (WPT) as preprocessed tools, was proposed to carry out the simultaneous voltammetric determination of Pb(II), Tl(I) and In (III) for the first time. This method combines the ideas of OSC and WPT with PLS regression for enhancing the...
This paper suggests a novel method named DF-LS-SVM based on least squares support vector machines (LS-SVM) regression combine with multiscale wavelet transforms and data fusion (DF) to enhance the ability to extract characteristic information and improve the quality of the regression. Experimental results showed the DF-LS-SVM method was successful for simultaneous multicomponent determination even...
This paper addressed multivariate calibration based on least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. LS-SVM technique have the advantages to provide the capability of learning a high dimensional feature with fewer training data, and to decrease the computational complexity for requiring only solving a set of linear equation...
A novel method named DF-PLS based on partial least squares (PLS) regression combined with data fusion (DF) was applied to enhance the ability of extracting characteristic information and the quality of regression for the simultaneous spectrophotometric determination of Cu(II), Ni(II) and Cr(II). Data fusion is a technique that seamlessly integrates information from disparate source to produce a single...
This paper suggests a novel method named DOSCWTGRNN, which is based on generalized regression neural network (GRNN) with direct orthogonal signal correction (DOSC) and wavelet transform (WT) as a preprocessing tool for the simultaneous spectrophotometric determination of o-nitro-aniline, m-nitro-aniline and p-nitro- aniline. DOSC was applied to remove structured noise that is unrelated to the concentration...
A novel method named OSCWPTPLS approach based on partial least squares (PLS) regression with orthogonal signal correction (OSC) and wavelet packet transform (WPT) as preprocessed tools was proposed for the simultaneous spectrophotometric determination of Al(III), Mn(II) and Co(II). This method combines the ideas of OSC and WPT with PLS regression for enhancing the ability of extracting characteristic...
This paper suggests a novel method named WTGRNN, which is based on generalized regression neural network (GRNN) with wavelet transform (WT) as a pre-processing tool for the simultaneous spectrophotometric determination of o-nitro-aniline, m-nitro-aniline and p-nitro-aniline. Wavelet representations of signals provide a local time-frequency description, thus in the wavelet domain, the quality of noise...
This paper presented a novel method named wavelet packet transform based generalized regression neural network (WPTGRNN) for studying the speciation of iron. The method combines wavelet packet thresholding denoising with generalized regression neural network. Wavelet packet representations of signals provided a local time-frequency description and separation ability between information and noise....
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