NIR Inversion Model of Larch Wood Density at Different Moisture Contents Based on MVO-BPNN

IF 0.8 4区 化学 Q4 SPECTROSCOPY
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.

基于 MVO-BPNN 的不同含水率下落叶松木材密度的近红外反演模型
由于含水率的变化,落叶松木材基本密度近红外(NIR)预测模型的准确性和鲁棒性降低,甚至模型失效。为解决这一技术难题,提出了多逆算法优化 BP 神经网络(MVO-BPNN)预测模型,以提高模型的准确性。比较了萨维茨基-戈莱平滑法、去趋势法和 15 点移动平均平滑法的预处理效果。使用协同区间偏最小二乘法提取近红外光谱的特征波段。结果表明,基于 MVO-BPNN 的预测模型优于基于 BPNN 和遗传算法优化的 BPNN 预测模型。这表明基于 MVO-BPNN 的近红外模型可以有效地预测不同含水率木材的基本密度。
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来源期刊
CiteScore
1.30
自引率
14.30%
发文量
145
审稿时长
2.5 months
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.
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