• 论文与研究报告 •

### 基于灰狼算法SVM的NIR杉木密度预测

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

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.