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A Novel Adaptive Robust NIR Modeling Method Based on Sparse Bayesian Learning
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DOI：10.1109/TII.2024.3367007.png)
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Abstract
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The near-infrared (NIR) method has shown great potential in estimating key parameters in various industrial processes. Selecting characteristic wavelengths from high-dimensional spectra is crucial in building a prediction model with the satisfactory performance. However, existing wavelength selection methods based on the wavelength or waveband importance estimation are time-consuming and unstable. To improve the stability and generalization of the established model, a novel adaptive robust method is proposed for NIR modeling in this work, in which the pattern-coupled sparse model has been developed to estimate the spectral peak wavebands adaptively. Furthermore, a robust method based on global-local shrinkage is developed for characteristic wavelength selection. Compared with the state-of-the-art techniques, the proposed method is more accurate and robust for NIR modeling on both benchmark and real datasets.
Keywords:
Characteristic wavelength selection
robust modeling
sparse Bayesian learning (SBL)
waveband estimation
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