风雹灾害是造成农业生产设施破坏和经济损失的主要自然灾害之一,因此有必要构建一个高效且准确的冰雹冲击力预测模型。本研究以拱形温室大棚薄膜风雹耦合试验为基础,采用粒子群优化(Particle Swarm Optimization, PSO)算法与反向传播(Ba...风雹灾害是造成农业生产设施破坏和经济损失的主要自然灾害之一,因此有必要构建一个高效且准确的冰雹冲击力预测模型。本研究以拱形温室大棚薄膜风雹耦合试验为基础,采用粒子群优化(Particle Swarm Optimization, PSO)算法与反向传播(Back Propagation, BP)神经网络相结合的方法,构建一个高效且准确的冰雹冲击力预测模型。该模型的平均绝对误差为0.22929,平均偏差误差为−0.09017,确定系数为0.99704。相较于传统线性回归预测方法,该模型可处理大数据量,适应性强,拟合效果好,且避免了传统BP模型容易陷入局部最小的缺点。Hail disasters are one of the major natural hazards causing damage to agricultural production facilities and economic losses, necessitating the development of an efficient and accurate hail impact force prediction model. This study employs a PSO-BP neural network approach, grounded in wind-hail coupling experiments on arched greenhouse films. The resultant model demonstrates superior performance with a mean absolute error (MAE) of 0.22929, a mean bias error (MBE) of −0.09017, and a determination coefficient (R2) of 0.99704. It surpasses traditional linear regression methods in handling large datasets, adaptability, fitting accuracy, and mitigating the issue of local minima in BP models.展开更多
粒子群优化算法(Particle Swarm Optimization, PSO)作为一种高效的全局优化算法,已被广泛应用于温室系统模型的优化与控制中,近年来在温室系统计算流体动力学(Computational Fluid Dynamics, CFD)模型中也得到了应用。本文综述了粒子...粒子群优化算法(Particle Swarm Optimization, PSO)作为一种高效的全局优化算法,已被广泛应用于温室系统模型的优化与控制中,近年来在温室系统计算流体动力学(Computational Fluid Dynamics, CFD)模型中也得到了应用。本文综述了粒子群算法的基本原理及其在温室系统CFD模型中的应用进展,分析总结了粒子群算法在温室系统CFD模型中应用的特点、优势,以及面临的挑战,并对未来的研究方向提出了展望。Particle Swarm Optimization (PSO), as an efficient global optimization algorithm, has been widely used in the optimization and control of greenhouse system models. In recent years, it has also been applied in Computational Fluid Dynamics (CFD) models of greenhouse systems. This article summarizes the basic principles of particle swarm optimization algorithm and its application progress in greenhouse system CFD models. It analyzes and summarizes the characteristics, advantages, and challenges of particle swarm optimization algorithm in greenhouse system CFD models, and puts forward prospects for future research directions.展开更多
文摘风雹灾害是造成农业生产设施破坏和经济损失的主要自然灾害之一,因此有必要构建一个高效且准确的冰雹冲击力预测模型。本研究以拱形温室大棚薄膜风雹耦合试验为基础,采用粒子群优化(Particle Swarm Optimization, PSO)算法与反向传播(Back Propagation, BP)神经网络相结合的方法,构建一个高效且准确的冰雹冲击力预测模型。该模型的平均绝对误差为0.22929,平均偏差误差为−0.09017,确定系数为0.99704。相较于传统线性回归预测方法,该模型可处理大数据量,适应性强,拟合效果好,且避免了传统BP模型容易陷入局部最小的缺点。Hail disasters are one of the major natural hazards causing damage to agricultural production facilities and economic losses, necessitating the development of an efficient and accurate hail impact force prediction model. This study employs a PSO-BP neural network approach, grounded in wind-hail coupling experiments on arched greenhouse films. The resultant model demonstrates superior performance with a mean absolute error (MAE) of 0.22929, a mean bias error (MBE) of −0.09017, and a determination coefficient (R2) of 0.99704. It surpasses traditional linear regression methods in handling large datasets, adaptability, fitting accuracy, and mitigating the issue of local minima in BP models.
文摘粒子群优化算法(Particle Swarm Optimization, PSO)作为一种高效的全局优化算法,已被广泛应用于温室系统模型的优化与控制中,近年来在温室系统计算流体动力学(Computational Fluid Dynamics, CFD)模型中也得到了应用。本文综述了粒子群算法的基本原理及其在温室系统CFD模型中的应用进展,分析总结了粒子群算法在温室系统CFD模型中应用的特点、优势,以及面临的挑战,并对未来的研究方向提出了展望。Particle Swarm Optimization (PSO), as an efficient global optimization algorithm, has been widely used in the optimization and control of greenhouse system models. In recent years, it has also been applied in Computational Fluid Dynamics (CFD) models of greenhouse systems. This article summarizes the basic principles of particle swarm optimization algorithm and its application progress in greenhouse system CFD models. It analyzes and summarizes the characteristics, advantages, and challenges of particle swarm optimization algorithm in greenhouse system CFD models, and puts forward prospects for future research directions.
