Zhiyuan Wang, Zheyu Zhang, Roger A. Williams, Yaoxiang Li
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引用次数: 0
Abstract
Owing to variation of moisture content, the larch wood basic density near-infrared (NIR) prediction model shows reduced accuracy and robustness, or even model failure. To solve this technical problem, a multi-verse-algorithm-optimized BP neural network (MVO-BPNN) prediction model is proposed to improve the accuracy of the model. The preprocessing effects of the Savitzky–Golay smoothing, detrending, and 15-point moving average smoothing method were compared. The synergy interval partial least squares was used to extract the feature bands of the NIR spectra. Results showed that the prediction model based on MVO-BPNN was better than those based on BPNN and the genetic algorithm-optimized BPNN. It indicated that the NIR model based on the MVO-BPNN could effectively predict the basic density of wood with different moisture contents.
期刊介绍:
Journal of Applied Spectroscopy reports on many key applications of spectroscopy in chemistry, physics, metallurgy, and biology. An increasing number of papers focus on the theory of lasers, as well as the tremendous potential for the practical applications of lasers in numerous fields and industries.