中文摘要：

根据木材在不同影响因素（密度、含水率和比重）下沿横纹方向（包括径向和弦向）的导热系数的实测数据集，应用基于粒子群算法（PSO）寻优的支持向量回归（SVR）方法，建立了木材沿不同方向的导热系数的预测模型，并与通过类比法（ANA）导出的理论模型和BP神经网络（BPNN）模型进行了比较。结果表明：基于相同的训练样本和检验样本，木材导热系数的SVR模型比其ANA模型或BPNN模型具有更高的预测精度；增加i）rI练样本数有助于提高sVR预测模型的泛化能力；基于留一交叉验证法（LOOCV）的SVR模型预测的最大绝对百分误差（MPE）、平均绝对误差（MAE）和平均绝对百分误差（MAPE）均为最小。因此，SVR是一种预测木材导热系数的有效方法。

英文摘要：

The support vector regression （SVR） method combined with particle swarm optimization （PSO） is proposed to establish a model for predicting the thermal conductivity of timber in transverse directions （radial direction and tangential direction） based on the measuring database of thermal conductivity under different influential factors, including its density, moisture content and specific gravity. Comparing the prediction results of SVR method with those from analogism （ANA） model and BP neural network （BPNN） model, it is shown that the prediction precision is higher for SVR method by applying identical training and test samples and increase of training samples could improve the generalization ability. With the validation test by leave--one--out cross validation （LOOCV） test, maximal absolute percentage error （MPE）, mean absolute error （MAE） and mean absolute percentage error（MAPE）, are the smallest for the prediction of SVR method. It is suggested that SVR is an effective and powerful tool for predicting thermal conductivity of timber.