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
机械钻速(rate of penetration,ROP)是钻井作业优化和减少成本的关键因素,钻井时有效地预测ROP是提升钻进效率的关键。由于井下钻进时复杂多变的情况和地层的非均质性,通过传统的ROP方程和回归分析方法来预测钻速受到了一定的限制。为...机械钻速(rate of penetration,ROP)是钻井作业优化和减少成本的关键因素,钻井时有效地预测ROP是提升钻进效率的关键。由于井下钻进时复杂多变的情况和地层的非均质性,通过传统的ROP方程和回归分析方法来预测钻速受到了一定的限制。为了实现对钻速的高精度预测,对现有BP (back propagation)神经网络进行优化,提出了一种新的神经网络模型,即动态自适应学习率的粒子群优化BP神经网络,利用录井数据建立目标井预测模型来对钻速进行预测。在训练过程中对BP神经网络进行优化,利用启发式算法,即附加动量法和自适应学习率,将两种方法结合起来形成动态自适应学习率的BP改进算法,提高了BP神经网络的训练速度和拟合精度,获得了更好的泛化性能。将BP神经网络与遗传优化算法(genetic algorithm,GA)和粒子群优化算法(particle swarm optimization,PSO)结合,得到优化后的动态自适应学习率BP神经网络。研究利用XX8-1-2井的录井数据进行实验,对比BP神经网络、PSO-BP神经网络、GA-BP神经网络3种不同的改进后神经网络的预测结果。实验结果表明:优化后的PSO-BP神经网络的预测性能最好,具有更高的效率和可靠性,能够有效的利用工程数据,在有一定数据采集量的区域提供较为准确的ROP预测。展开更多
将粒子群优化(PSO)算法与BP神经网络相结合,应用在传感器静态非线性特性的校正中.用PSO算法所得到的全局最优值作为BP神经网络的初始权值,训练BP神经网络,训练结束后的神经网络作为传感器的静态特性校正器.应用结果表明,该方法可以提高B...将粒子群优化(PSO)算法与BP神经网络相结合,应用在传感器静态非线性特性的校正中.用PSO算法所得到的全局最优值作为BP神经网络的初始权值,训练BP神经网络,训练结束后的神经网络作为传感器的静态特性校正器.应用结果表明,该方法可以提高BP神经网络的精度,并且该神经网络具有良好的泛化能力.
Abstract：
A static nonlinear errors method for correcting the sensors based on BP neural network using particle swarm optimization (PSO) is described. The global best values of particle swarm are used as initial weights of BP neural network to train BP neural network. Then the trained neural network is regarded as the sensor's corrector. The application results show that this method can improve the precision of the BP neural network, and the generalization capability of the neural network is good.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.
文摘将粒子群优化(PSO)算法与BP神经网络相结合,应用在传感器静态非线性特性的校正中.用PSO算法所得到的全局最优值作为BP神经网络的初始权值,训练BP神经网络,训练结束后的神经网络作为传感器的静态特性校正器.应用结果表明,该方法可以提高BP神经网络的精度,并且该神经网络具有良好的泛化能力.
Abstract：
A static nonlinear errors method for correcting the sensors based on BP neural network using particle swarm optimization (PSO) is described. The global best values of particle swarm are used as initial weights of BP neural network to train BP neural network. Then the trained neural network is regarded as the sensor's corrector. The application results show that this method can improve the precision of the BP neural network, and the generalization capability of the neural network is good.
