摘要
使用900~1700 nm近红外光谱分析仪对展平竹进行多点采样,随后利用深度置信网络(DBN)与局部支持向量回归(LSVR)进行训练,建立DBN_LSVR预测模型来预测单元材料的力学性能。采集到的光谱数据经过S-G滤波,完成了数据平滑,去除了大部分噪声。DBN-LSVR预测模型的竹材断裂模量(MOR)和弹性模量(MOE)的均方根误差分别为716.23、17.54,平均绝对误差分别为524.40、16.82。将预测误差与BP、DBN、LSVR模型预测误差比较,DBN_LSVR模型的预测误差最小。基于DBN_LSVR的MOR和MOE预测模型的相关系数分别为0.85和0.80。提出的方法可以有效地对展平竹力学强度进行无损分析,缩短了判别竹材物理性能的时间和成本。
With near infrared spectroscopy(NIRS),a nondestructive and fast method for mechanical strength prediction of bamboo was studied.A 900-1700 nm NIR analyzer was used to conduct multi-point sampling on flattened bamboo.Then,deep confidence network(DBN)and local support vector regression(LSVR)were used for training,and a DBN_LSVR prediction model was established to predict the mechanical properties of the unit material.The collected spectral data were smoothed by S-G filtering,and most of the noise was removed.The root mean square errors of bamboo’s MOR and MOE based on the DBN_LSVR prediction model are 716.23 and 17.54,respectively,the mean absolute errors are 524.40,16.82,respectively.Compared with BP,DBN,LSVR model prediction error,DBN_LSVR model prediction error is the smallest.The correlation coefficients of MOR and MOE prediction models based on DBN_LSVR are 0.85 and 0.80,respectively.The proposed method can effectively analyze the mechanical strength of flattened bamboo without damage,and shorten the time and cost of distinguishing the physical properties of bamboo.
作者
李超
陈勋
张立新
马心雨
黄英来
Li Chao;Chen Xun;Zhang Lixin;Ma Xinyu;Huang Yinglai(Northeast Forestry University,Harbin 150040,P.R.China)
出处
《东北林业大学学报》
CAS
CSCD
北大核心
2022年第3期103-108,共6页
Journal of Northeast Forestry University
基金
黑龙江省自然科学基金项目(LH2020C051)。
关键词
近红外光谱
力学强度
深度学习
Near infrared spectrum
Mechanical strength
Deep learning