Mechanical Properties of Wood Prediction Based on the NAGGWO-BP Neural Network

Abstract

The existing original BP neural network models for wood performance prediction have low fitting accuracy and imprecise prediction results. We propose a nonlinear, adaptive grouping gray wolf optimization (NAGGWO)-BP neural network model for wood performance prediction. Firstly, the original gray wolf optimization (GWO) algorithm is optimized. We propose CPM mapping (the Chebyshev mapping method combined with piecewise mapping followed by mod operation) to generate the initial populations and improve population diversity, and an ‘S’-type nonlinear control parameter is proposed to balance the exploitation and exploration capabilities of the algorithm; an adaptive grouping strategy is also proposed, based on which the wolves are divided into the predator, wanderer, and searcher groups. The improved differential evolution strategy, the stochastic opposition-based learning strategy, and the oscillation perturbation operator are used to update the positions of the wolves in the different groups to improve the convergence speed and accuracy of the GWO. Then, the BP neural network weights and thresholds are optimized using the NAGGWO algorithm. Finally, we separately predicted heat-treated wood’s five main mechanical property parameters using different models. The experimental results show that the proposed NAGGWO-BP model significantly improved the mean absolute error (MAE), the mean square error (MSE), and the mean absolute percentage error (MAPE) of the specimens, compared with the BP, GWO-BP, and TSSA-BP algorithms. Therefore, this model has strong generalization ability and good prediction accuracy and reliability, which can fully meet practical engineering needs.