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To take advantages of multiscale property of near infrared (NIR) spectra, a new hybrid algorithm (GA-WPLS) was proposed for developing the multivariate regression model in the wavelet domain instead of the spectra domain. At first, wavelet packet transform (WPT) algorithm and its reconstruction algorithm are employed to split the raw spectra into different frequency components in wavelet domain. Then...
This study reports the use of a kernel-based process model, consisting of kernel partial least squares regression and kernel ridge regression, to model etch rate and uniformity in a plasma etch process. In order to characterize the plasma etch process, a 24 - 1 fractional factorial design was implemented on the process parameters: CHF3 flow rate, CF4 flow rate, RF power, and pressure. In this modeling,...
The aim of this paper is to find a model to forecast 1-month ahead monthly sardines catches using a multivariate polynomial model combined with multi-scale stationary wavelet decomposition. The observed monthly sardines catches are decomposed into various sub-series employing wavelet decomposition and then appropriate sub-series are used as inputs to the autoregressive forecasting model. The forecasting...
This study combines wavelet-based feature extractions with kernel partial least square (PLS) regression for international stock index forecasting. Wavelet analysis is utilized as a preprocessing step to decompose and extract most important time scale features from high dimensional input data. Owing to the high dimensionality and heavy multi-collinearity of the input data, a kernel PLS regression model...
Wavelet transformation is performed on NIR transmittance and Raman spectroscopic data followed by prediction of active substance content of pharmaceutical tablet from the spectral data. Partial least squares regression (PLSR) is used to build the prediction models. Comparison is made between prediction models with and without wavelet compression. Results show that wavelet-transformed NIR spectral...
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