Model-based Transfer Learning for Prediction of Multi-factor Wood Thermal Conductivity
Wood thermal conductivity is important for accurate prediction of energy consumption in timber structure;however,current models in the literature are difficult to provide comprehensive and accurate prediction tools,and some machine learning models have limited factors and sample sizes to consider.In this paper,a database of thermal conductivity coefficients containing multiple features from 1941 to the present was constructed,and a comparison of the prediction accuracy of neural networks and transfer learning models was developed.The results show that the prediction accuracy of thermal conductivity can be improved by more than 23％after combining transfer learn-ing,and the transfer learning performance is significantly improved on 0～0.3 W·m-1·K-1,which is more suitable for heat transfer applications such as timber structures.The novel transfer strategy with fine-tuning can provide ideas for thermal conductivity prediction of small samples,data sets with large number of missing values and simple network structures.
NETL
NSTL
万方数据
