Carnivorous Plant Algorithm and BP to Predict Optimum Bonding Strength of Heat-Treated Woods

Abstract

In this study, the CPA algorithm was used to optimize a BP neural network model to predict the bond strength and surface roughness of heat-treated wood. The neural network model was trained and optimized using MATLAB software. The results of the BP neural network, random forest algorithm, and optimized CPA-BP model were compared. The results show that the CPA-optimized BP neural network model has a better R2 compared to the conventional BP neural network model. After using the CPA-optimized BP neural network model, the R2 value increased by 8.1%, the MAPE value decreased by 3.74%, and the MAE value decreased by 33.91% in the prediction of the surface bond strength. The R2 values increased by 3.02% and 20.47%, respectively, in predicting the mean and maximum values of surface roughness. The results indicate that the model is reliable in predicting wood bond strength and wood surface roughness. Using this model to predict wood bond strength and surface roughness can also reduce the required experimental cost.