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林业科学 ›› 2018, Vol. 54 ›› Issue (12): 137-141.doi: 10.11707/j.1001-7488.20181215

• 论文与研究报告 • 上一篇    下一篇

基于灰狼算法SVM的NIR杉木密度预测

谭念1, 王学顺1, 黄安民2, 王晨2   

  1. 1. 北京林业大学理学院 北京 100083;
    2. 中国林业科学研究院木材工业研究所 北京 100091
  • 收稿日期:2017-07-10 修回日期:2018-01-08 出版日期:2018-12-25 发布日期:2018-12-11
  • 基金资助:
    中央高校基本科研业务费专项资金资助项目(2015ZCQ-LY-01);国家自然科学基金项目(31670564)。

Wood Density Prediction of Cunninghamia lanceolata Based on Gray Wolf Algorithm SVM and NIR

Tan Nian1, Wang Xueshun1, Huang Anmin2, Wang Chen2   

  1. 1. School of Science, Beijing Forestry University Beijing 100083;
    2. Research Institute of Wood Industry, CAF Beijing 100091
  • Received:2017-07-10 Revised:2018-01-08 Online:2018-12-25 Published:2018-12-11

摘要: [目的]提出一种基于灰狼算法支持向量机(GWO-SVM)的木材密度预测模型,利用近红外光谱(NIR)对杉木密度进行预测,为杉木性质定量分析提供理论依据。[方法]将109个杉木样品光谱数据和样品密度数据进行归一化,选取88个样品作为训练集、21个样品作为测试集,对2 151维光谱数据提取主成分,以主成分作为输入变量,以杉木样本密度作为输出变量,建立杉木密度多元线性回归(MLR)模型、SVM模型和GWO-SVM模型,采用决定系数(R2)、均方误差(MSE)和平均绝对百分误差(MAPE)对3种模型的预测结果进行比较分析。[结果]对光谱数据进行主成分分析并选择5个主成分,其累积贡献率达98.7%。MLR模型的R2为0.771 4,MSE为0.000 282 1,MAPE为3.009 23%;SVM模型的R2为0.923 8,MSE为0.000 233 1,MAPE为2.794 50%;灰狼算法对SVM进行参数寻优,获得的最优参数分别为C=18.366 6、σ=0.043 3,GWO-SVM模型的R2为0.919 2,MSE为0.000 183 4,MAPE为2.496 37%。3种模型的平均绝对百分误差均在可接受范围内,且GWO-SVM模型的平均绝对百分误差最小,预测效果最好。[结论]从预测精度分析,GWO-SVM模型明显优于MLR模型和SVM模型;从模型决定系数分析,GWO-SVM模型和SVM模型均优于MLR模型。灰狼算法优化支持向量机结合近红外光谱对杉木密度进行预测分析合理、高效。

关键词: 近红外光谱, 灰狼算法, 支持向量机, 杉木密度

Abstract: [Objective] In order to explore the more efficient method of predicting the wood density of Cunninghamia lanceolata, the near infrared spectroscopy was used. It could provide the theoretical basis for quantitative analysis of wood properties.[Method] Firstly, wood density and the near infrared spectroscopy data of 109 C. lanceolata samples were normalized. Of which,88 C. lanceolata samples constituted training set and 21 samples constituted test set. Secondly, the principal component analysis was used to extract the principal components of 2 151 dimensions of the C. lanceolata near infrared spectrum. Thirdly, the principal components set as the independent variable and the C. lanceolata density set as the dependent variable were used to establish the multiple linear regression(MLR)model and the support vector machine(SVM)model. In order to improve the prediction accuracy of C. lanceolata density model, the grey wolf optimizer(GWO)algorithm was applied to the SVM model for parameter optimization. Therefore the prediction of the C. lanceolata density based on the GWO-SVM was proposed. The determination coefficient(R2), the mean square error(MSE)and the mean absolute percentage error(MAPE)were adopted to measure the prediction result of the three models.[Result] Five principal components were obtained from the near infrared spectrum, and their cumulative contribution rate was 98.7%. The significant value P of the MLR model and the partial regression coefficient was both less than 0.05, which indicated that the MLR model was effective and the model can be used for the C. lanceolata density prediction. The R2 of MLR model was 0.771 4, the MSE was 0.000 282 1, and the MAPE was 3.009 23%. At the same time, the R2 of SVM model was 0.923 8, the MSE was 0.000 233 1, and the MAPE was 2.794 50%. The parameters of the SVM were optimized by the wolf group algorithm, and the optimal parameters were C=18.366 6, σ=0.043 3. In the GWO-SVM model, the R2 was 0.919 2, the MSE was 0.000 183 4, and the MAPE was 2.496 37%.The MAPE of the three models were all within the acceptable range, and the prediction of the GWO-SVM model was the best.[Conclusion] The GWO-SVM model is superior to the MLR model and the SVM model on the prediction accuracy. The GWO-SVM and the SVM model are superior to the MLR model on determination coefficient analysis. So the approach of GWO-SVM combined with the near infrared spectroscopy to predict the C. lanceolata density is reasonable and efficient.

Key words: near infrared spectrum, grey wolf optimizer(GWO), support vector machines(SVM), wood density of Cunninghamia lanceolata

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