# Drying Stress and Strain of Wood: A Review
## 5 Artificial Neural Network and Its Application in the Field of Wood Drying
### Application of ANNs in the Field of Wood Drying  
The application of ANNs in the field of wood science is late, but has a rapid development. Researchers have successively confirmed the powerful simulation and prediction capabilities of artificial neural networks:  
[PERSON]'s research team made use of ANNs to estimate the thermal conductivity of wood under conditions of different MC, temperature and apparent density [30]. In the following year, a neural network that could predict the dielectric loss factor of wood based on the basic chemical properties of wood was successfully developed [102]. They also discussed the feasibility of artificial network models in predicting the static bending strength, elastic modulus and density of wood [103; 104]. [PERSON]'s research group tested wood MC, density and ultrasonic propagation speed to predict the elastic modulus of fir; then, four neural networks were established to predict and determine the physical and mechanical properties of plywood (board thickness, MC, specific gravity, bending strength and MOE) [31; 105]. [PERSON]'s team studied the strength properties of thermally modified wood. The method adopted was to establish a model with the input parameters of wood type, heat treatment temperature and heat treatment time to predict the compressive strength, elastic  
Figure 10: BP neural network structure. modulus and modulus of rupture of heat-treated wood. The results suggest that compared with multiple regression analysis, the ANN model has good predictive performance [106]. After that, they also simulated and predicted the modulus of rupture and modulus of elasticity of the particleboard under different pressing conditions [107]. [PERSON] et al. used temperature, treatment time and wood type as the input layer, and wood hardness as the output layer [108]. The measurement coefficient (R2) of all data sets obtained is greater than 0.99, and the prediction is more effective. In the following year, they once again proved the plasticity and developability of the artificial neural network model by predicting the color change of heat-treated wood during the artificial weathering process [109]. In the aspect of wood drying, the ANN model also shows its predictive effect. The researchers used wood MC and drying stress as the output variable matrix, and made a reasonable three-layer ANN model through the BP neural network algorithm, aiming to predict the characteristics of wood drying more accurately. [PERSON] et al. successively determined the diffusion of water under non-isothermal conditions by using ANNs [11, 110]. [PERSON] combined artificial neural networks and mathematical models to predict and analyze the MC of poplar and pine [111]. [PERSON] established a time-delay neural network model to explore the correlation between wood MC, drying temperature and humidity. Meanwhile, she tracked and predicted wood MC dynamically [112, 113]. [PERSON] et al. used ANN theory to simulate and predict the final MC of air-dried wood by setting the input variables of the initial MC, basic density, annual ring orientation, annual ring width, heartwood ratio and wood lightness, and compared with the principal component regression model. They found the ANN prediction model has better performance, and the predicted value is closer to the actual measured value [12]. Based on the near-infrared spectroscopy technology, they established an artificial neural network model to predict and analyze the dry stress on the wood surface [114]. Compared with the partial least squares model, the time required to reach the maximum tensile stress and the direction of the stress reversal of the near-infrared spectroscopy model are basically anastomotic with the experiment value. The prediction of the near-infrared spectroscopy model is better. [PERSON] tried to predict the value of the radio frequency heating rate and found that the MC has a greater impact on the radio frequency heating rate [115].