Mechanical Property Prediction of Wood Using a Backpropagation Neural Network Optimized by Adaptive Fractional-Order Particle Swarm Algorithm

This study proposes a novel LK-BP-AFPSO model for the nondestructive evaluation of wood mechanical properties, combining a backpropagation neural network (BP) with adaptive fractional-order particle swarm optimization (AFPSO) and Liang–Kleeman (LK) information flow theory. The model accurately predi...

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Bibliographic Details
Published in:	Forests Vol. 16; no. 8; p. 1223
Main Authors:	Huang, Jiahui, Kuang, Zhufang
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.08.2025
Subjects:	Accuracy Algorithms Analysis Artificial intelligence Back propagation Back propagation networks Bend strength Compressive strength Efficiency Feature selection Flow theory Information flow Information management Machine learning Mathematical optimization Mechanical properties Methods Modulus of elasticity Neural networks Nondestructive testing Optimization algorithms Particle swarm optimization Performance evaluation Quality assessment Quality control Shear strength Tensile strength Wood China
ISSN:	1999-4907, 1999-4907
Online Access:	Get full text
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