Compression strength prediction of Xylosma racemosum using a transfer learning system based on near-infrared spectral data

作     者：Guangyu Shi Jun Cao Chao Li Yuliang Liang Guangyu Shi;Jun Cao;Chao Li;Yuliang Liang

作者机构：Northeast Forestry UniversityHarbin 150040People’s Republic of China 

出 版 物：《Journal of Forestry Research》 (林业研究（英文版）)

年 卷 期：2020年第31卷第3期

页      面：1061-1069页

核心收录：

学科分类：12[管理学] 082902[工学-木材科学与技术] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0829[工学-林业工程] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：fully funded by the Program of National Natural Science Foundation of China(CN)(31700643) Fundamental Research Funds for the Central Universities(2572015AB24) 

主　　题：Xylosma racemosum Compression strength prediction Near-infrared spectroscopy Transfer learning system TCA–PCA 

摘      要：A transfer learning system was designed to predict Xylosma racemosum compression ***-infrared(NIR)spectral data for Acer mono and its compression strength values were used to resolve the weak generalization problem caused by using a *** dataset *** component analysis and principal component analysis are domain adaption and feature extraction processes to enable the use of *** NIR spectral data to design the transfer learning system.A five-layer neural network relevant to the *** dataset,was fine-tuned using the *** *** were 109 *** samples used as the source dataset and 79 *** samples as the target *** the ratio of the training set to the test set was 1:9,the correlation coeffi cient was 0.88,and mean square error was *** results show that NIR spectral data of hardwood species are *** the mechanical strength of hardwood species using multi-species NIR spectral datasets will improve the generalization ability of the model and increase accuracy.