落叶松木材干燥的BP神经网络模型研究  

Research on BP Neural Network Model of Larch Wood Drying

作　　者：马翔宇[1] 王晓丰[1] 段文英[1] 

机构地区：[1]东北林业大学理学院,哈尔滨150040

出　　处：《森林工程》2015年第1期63-65,共3页Forest Engineering

基　　金：中央高校基本科研业务费专项资金项目(2572014BB20)

摘　　要：以落叶松木材为研究对象,实验在东北林业大学干燥实验室进行,采用MATLAB中log-sigmoid型函数(logsig)和线性函数(purelin)为神经元的作用函数,用落叶松木材的干燥温度、湿度、循环风速及平衡含水率作为输入变量,以木材含水率作为输出变量,构建了4∶S∶1的木材干燥的BP人工神经网络模型。用120组数据对网络模型进行训练及检验,得最适宜的网络结构为4∶10∶1,均方误差函数mse=0.001 7,总体拟合精度为96.86%。该模型能够运用到相同条件下的其他树种的木材干燥。Taking the larch wood as the research object,the experiment is conducted in the drying laboratory at Northeast Forestry University. Log-sigmoid-type function（ logsig） and a linear function（ purelin） in MATLAB are used as the role of neuronal function.Drying temperature,humidity,wind speed and equilibrium moisture content of the loop of larch wood are used as input variables,meanwhile the moisture content of wood as output variables,BP artificial neural network mode of 4 ∶ S∶ 1 wood drying is constructed.The network model is trained and tested by 120 groups of data. The results showed that the most suitable network structure is 4∶ 10∶1,the mean square error function MSE is 0. 0017,the general fitting accuracy was 96. 86%. The model can be applied to other species under the same conditions of wood drying.

关 键 词：BP神经网络 落叶松木材 干燥 木材含水率 

分 类 号：S782.31[农业科学—木材科学与技术]