木材弹性模量估计的混合粒子群算法

Ming-Bao Li, Jiawei Zhang
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引用次数: 0

摘要

提出了一种基于神经网络构建的粒子群优化算法,用于校正木材弹性模量与木材物性参数之间复杂的非线性关系。针对传统BP算法收敛速度慢、容易陷入局部极小值的缺点,采用粒子群优化(PSO)和反向传播(BP)的混合算法对神经网络进行训练。建模和仿真结果表明,基于粒子群建模方法的优化技术是可行和有效的,具有较高的模型泛化能力和预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid pso algorithm for estimation modulus of elasticity of wood
Particle swarm optimization algorithm based neural network construction has been presented to calibrate the complex nonlinear relationship between modulus of elasticity (MOE) and wood physical property parameters. Consider that the traditional BP algorithm has shortcomings of converging slowly and easily trapping a local minimum value, a hybrid algorithm using particle swarm optimization (PSO) and back propagation (BP) is adopted to train the neural network. Modeling and Simulation results show that the optimization technique based on PSO modeling method is feasible and effective, with high generalization ability of the model and forecast accuracy.
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