Analysis of Factors Related to Vasovagal Response in Apheresis Blood Donors and the Establishment of Prediction Model Based on BP Neural Network Algorithm

Objective:To analyze the factors related to vessel vasovagal reaction(VVR)in apheresis donors,establish a mathematical model for predicting the correlation factors and occurrence risk,and use the prediction model to intervene in high-risk VVR blood donors,improve the blood donation experience,and retain blood donors.Methods:A total of 316 blood donors from the Xi'an Central Blood Bank from June to September 2022 were selected to statistically analyze VVR-related factors.A BP neural network prediction model is established with relevant factors as input and DRVR risk as output.Results:First-time blood donors had a high risk of VVR,female risk was high,and sex difference was significant(P value<0.05).The blood pressure before donation and intergroup differences were also significant(P value<0.05).After training,the established BP neural network model has a minimum RMS error of o.116,a correlation coefficient R=0.75,and a test model accuracy of 66.7%.Conclusion:First-time blood donors,women,and relatively low blood pressure are all high-risk groups for VVR.The BP neural network prediction model established in this paper has certain prediction accuracy and can be used as a means to evaluate the risk degree of clinical blood donors.

基于CSSA-BPNN模型的胶结充填体动态抗压强度预测

充填采矿法二步骤回采时胶结充填体稳定性受爆破扰动而降低。为快速准确地获得充填体动态抗压强度,利用分离式霍普金森压杆(SHPB)进行了40组不同应变率的单轴冲击实验,以灰砂比、充填体密度、养护龄期和平均应变率作为输入参数,充填体动态抗压强度作为输出参数,建立了一种基于Logistic混沌麻雀搜索算法(CSSA)优化BP神经网络(BPNN)的预测模型,并与传统BPNN和麻雀搜索算法优化的BPNN进行了对比分析。结果表明:CSSA-BPNN模型的平均相对误差为4.11%,预测值与实测值之间拟合的相关系数均在0.96以上,模型预测精度高。CSSA-BPNN模型的均方根误差为0.395 0 MPa,平均绝对误差为0.359 2 MPa,决定系数为0.995 2,均优于另外两种预测模型。实现了对充填体动态抗压强度的准确预测,可大幅减小物理实验量,为矿山胶结充填体的强度设计提供了一种新方法。

基于改进麻雀搜索算法优化BPNN的电阻点焊质量预测

电阻点焊技术由于具有高效、自动化程度高等焊接特点,被广泛应用于汽车、航空航天和公共交通等制造领域,由于焊点在封闭状态下进行,焊接过程存在诸多影响因素且无法直接检测,因此,准确预测电阻点焊质量是生产过程中必不可少的环节.本文以2219/5A06铝合金为研究对象,在3种不同的装配条件(包括间隙和间距)下进行电阻点焊工艺信号的分析,并进行人工智能建模.为了提高电阻点焊质量评价的性能和效率,本文采用Logistic-Tent(LT)复合映射改进麻雀搜索算法(SSA)对反向传播神经网络(LT-SSA-BPNN)模型进行优化,模型的输入和输出分别为多信号融合后的变量和熔核直径.实验结果表明,与传统的标准反向传播神经网络(BPNN)模型相比,经过LT-SSA-BP模型优化后,预测结果的平均绝对误差(MAE)、均方误差(MSE)和均方根误差(RMSE)分别降低了36.17%、17.55%和51.75%.同时,LT-SSA-BP神经网络在添加了不同间隙和间距条件作为训练集后,其预测稳定性明显提高,可以成功预测电阻点焊质量.

基于改进 PSO-BPNN 的拖拉机液压油品质监测

为实现对拖拉机液压油品质的有效监测,保障拖拉机液压系统的平稳运行,基于改进PSO-BPNN设计一种针对拖拉机液压油品质的监测方法。首先,为研究拖拉机液压油品质恶化情况,在液压油新油的基础上配制不同比例的液压油油样。随后,搭建拖拉机液压油品质监测试验装置,并依据试验装置采集与监测液压油粘度、介电常数和温度参数。然后,设计并搭建一种基于改进PSO-BPNN的拖拉机液压油品质监测模型,该模型利用正弦调整惯性权重的PSO算法优化BPNN的权值和阈值初始值,提高模型收敛效率。最后,为验证基于改进PSO-BPNN的液压油品质监测方法的可行性,与基于传统BPNN、标准PSO-BPNN的拖拉机液压油品质监测模型进行对比。结果表明,基于改进PSO-BPNN的拖拉机液压油品质监测方法具有较快的收敛速度,监测正确率达到97.78%,为优化拖拉机液压油品质监测方法提供参考。

基于MIV-PSO-BPNN的掘进面风温预测方法

目的为防治矿井热害,解决矿井掘进面风温预测问题,方法提出一种MIV算法优化的PSO-BPNN预测模型。通过利用MIV算法确定模型的输入变量,以BP网络建模,使用粒子群优化算法结合BP神经网络实现掘进工作面风流温度的预测,得到预测结果并与BPNN模型、PSO-BPNN模型、SVR模型相比较。结果结果表明:MIV-PSO-BPNN预测模型的相对误差为-0.47%~1.81%,分别优于PSO-BPNN、BPNN、SVR预测模型的-3.96%~1.93%,-5.54%~2.98%,-2.16%~2.95%,预测模型的误差为-0.1~0.5℃,表明预测值与实测值基本一致;与BPNN预测模型、PSO-BPNN预测模型、SVR预测模型相比,MIV-PSO-BPNN预测模型的预测结果平均绝对误差分别减少65%,54%,50%,均方误差分别减少88%,78%,69%,表明该预测模型的预测效果优于其他3种模型。结论所提模型适用于矿井掘进工作面风温的预测。

基于BPNN和MOOGA的高速联轴器多目标优化方法

针对高转速、复合工况下膜盘联轴器难以保证其强度特性问题,对已有膜盘联轴器强度及动力学特性进行了研究,提出了一种基于反向传播神经网络(BPNN)和多目标优化遗传算法(MOOGA)的高速联轴器多目标优化方法。首先,为了得到优化所需的关键参数,采用了正交实验结合多因素方差分析的方法,选取了联轴器优化参数;然后,基于已选取的关键参数,采用BPNN方法构建了截面应力和弯曲刚度的目标函数,并将其与多项式拟合方法进行了对比,对BPNN方法的精确性进行了验证;最后,采用MOOGA方法对目标函数进行了多目标优化,并将优化前后结果进行了对比分析。研究结果表明:采用BPNN结合MOOGA的方法对联轴器设计参数进行优化,在满足联轴器刚度需求的情况下,可有效降低联轴器膜盘的危险截面应力;优化后,联轴器危险应力减小了18.2%,弯曲刚度降低了5.05%,联轴器角向补偿能力增加了0.1°,从而证明了仿真的有效性。该结果可以为挠性联轴器参数优化设计提供参考。

基于MFO-BPNN的螺旋钻机钻速预测研究

针对利用现有经验公式所建立的螺旋钻机钻速预测模型存在准确度不足的问题,提出了一种基于飞蛾扑火算法(MFO)的反向传播神经网络(BPNN)钻速预测模型。首先,对MFO算法的基本原理进行了研究,构建了MFO算法优化BPNN的具体流程;接着,采集了江苏无锡某施工现场钻探数据,并分析了钻速影响因素,运用小波阈值降噪、归一化和灰色关联度分析等系列方法对采集数据进行了预处理,得到了训练和测试集;然后,将MFO算法运用于神经网络的权值和阈值训练,以代替原有梯度下降法,建立了MFO-BPNN钻速预测模型;最后,对上述预测模型与BPNN模型、遗传算法优化反向传播神经网络(GA-BPNN)模型以及粒子群优化算法优化反向传播神经网络(PSO-BPNN)模型的预测结果和评价指标进行了详细的对比分析。研究结果表明:运用MFO-BPNN建立的钻速预测模型,其可靠性达到了91.65%,其决定系数(R 2)优于其他3种预测模型,3项误差指标也是其中最低的,说明该模型的预测精度良好,适合于桩基础工程的实际应用,可为复杂因素影响下的钻速预测提供一种新思路。

基于MCDM-BPNN的城市内涝风险评价及调蓄池选址

为建立一套较为完善的城市内涝风险评价体系,并据此确定调蓄池位置,首先,从积水风险、超载风险和边侧进流量3个维度构建评价指标,设计一种包括改进层次分析法(IAHP)、反熵权法(AEW)和优劣解距离法(TOPSIS)的混合多准则决策框架(MCDM);然后,将IAHP-AEW-TOPSIS模型分别与IAHP-TOPSIS、AEW-TOPSIS模型对比,通过斯皮尔曼排序相关系数验证排序一致性,通过计算变异系数、相对极差和灵敏度证实IAHP-AEW-TOPSIS模型的性能;最后,结合反向传播神经网络(BPNN),建立MCDM-BPNN模型,并以山西省某一内涝易发区域为例进行验证。结果表明:积水风险对城市内涝风险评价体系的影响最为显著,所占权重为0.46,其次为超载风险,所占权重为0.36;节点位置与连接管道数量很大程度上对该节点的内涝风险产生影响,在管道汇接处或汇流面积较大处内涝出现更为频繁;IAHP-AEW-TOPSIS模型在样本判别方面具有更好的性能;在5年与10年重现期下,MCDM-BPNN模型验证集准确率分别为93.3%和100%,能够准确快速模拟和预测城市洪水;应用案例设置调蓄池后,高、中、低风险节点数量分别为7、9、30和6、19、21,内涝溢流削减效果显著。

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

基于BPNN-SHAP模型的滑坡危险性评价:以伊犁河流域为例

为进一步提高滑坡危险性预测模型精度、增强模型可解释性,本文以新疆伊犁河流域为研究区,选取8个影响滑坡发生的危险性因子,在反向传播神经网络(BPNN)基础上,借鉴博弈论思想,构建一种可解释BP神经网络模型(BPNNSHAP),解决神经网络滑坡危险性评价的“黑箱”问题。将数据集分为70%训练集和30%测试集,采用5折交叉验证提高模型稳定性,对比深度神经网络(DNN)、随机森林(RF)和逻辑回归(LR)3个模型的评价精度,并探讨BPNNSHAP预测结果的可解释性,完成区域滑坡危险性评价。研究结果表明:相较于其他模型,BPNN-SHAP模型的5个精度评价指标均为最高,分别是:准确率(A)=0.904、精准度(P)=0.911、召回率(R)=0.919、F1分数(F1_(Score))=0.915、曲线下面积(SAUC)=0.901;研究区滑坡极高、高危险区分别占比11.96%、15.53%,其中新源县和巩留县极高、高危险区占比最高,分别为51.1%、45.6%;滑坡主控因子为高程、坡度、降雨量和峰值地面加速度(PGA),定量揭示高程在1500~2000 m、坡度大于14°、年降雨量在260~310 mm、PGA大于0.23 g的区域对滑坡发生起促进作用,表明该区域滑坡可能为高程和坡度主控的降雨型、地震型滑坡。本研究方法可为滑坡危险性评价提供新的技术参考,为伊犁河流域防灾减灾韧性建设提供理论支撑。

基于GPT3和PSO-BPNN的欧洲地区天顶对流层延迟模型

利用粒子群优化算法(PSO)优化反向传播神经网络(BPNN),建立优化的GPT3模型(MGPT3)。以欧洲地区30个IGS测站2020年连续366 d的天顶对流层延迟(ZTD)数据为例进行实验,对比MGPT3、UNB3m和GPT3模型预测ZTD的精度。结果表明,MGPT3模型的RMSE为18.49 mm,相较于UNB3m和GPT3模型,其精度分别提高55.0%和47.7%。

基于SSA-BPNN的锂离子电池SOH估算

锂离子电池已被广泛应用于储能系统与电动汽车中,精确地估算锂离子电池健康状态SOH(state-of-health)是保证系统安全可靠运行的必要条件。从容量的角度分析SOH,在恒流-恒压CC-CV(constant current-constant voltage)充电电压和温度曲线中提取了7个健康特征HI(health indicator)作为输入,基于数据驱动法提出了麻雀搜索算法-反向传播神经网络SSA-BPNN(sparrow search algorithm-back propagation neural network)的锂离子电池SOH估算方法,并应用数据增强进一步提高模型的鲁棒性,最终在NASA锂离子电池随机使用数据集上进行验证。通过与未采取数据增强的传统BP神经网络相比,获得SOH估算精度有明显提升,测试集SOH估算的最大绝对误差和均方根误差分别小于3%和1.32%,实验结果表明该方法兼顾误差小,收敛快,全局搜索能力且能够适应电池老化差异特性。

An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.

Adaptive fuze-warhead coordination method based on BP artificial neural network

The appropriate fuze-warhead coordination method is important to improve the damage efficiency of air defense missiles against aircraft targets. In this paper, an adaptive fuze-warhead coordination method based on the Back Propagation Artificial Neural Network(BP-ANN) is proposed, which uses the parameters of missile-target intersection to adaptively calculate the initiation delay. The damage probabilities at different radial locations along the same shot line of a given intersection situation are calculated, so as to determine the optimal detonation position. On this basis, the BP-ANN model is used to describe the complex and highly nonlinear relationship between different intersection parameters and the corresponding optimal detonating point position. In the actual terminal engagement process, the fuze initiation delay is quickly determined by the constructed BP-ANN model combined with the missiletarget intersection parameters. The method is validated in the case of the single-shot damage probability evaluation. Comparing with other fuze-warhead coordination methods, the proposed method can produce higher single-shot damage probability under various intersection conditions, while the fuzewarhead coordination effect is less influenced by the location of the aim point.

Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel

Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management.Aiming at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved Sparrow Search Algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established.Taking 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN model.The results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison model.Using the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance.

Research on Narrowband Line Spectrum Noise Control Method Based on Nearest Neighbor Filter and BP Neural Network Feedback Mechanism

Thefilter-x least mean square(FxLMS)algorithm is widely used in active noise control(ANC)systems.However,because the algorithm is a feedback control algorithm based on the minimization of the error signal variance to update thefilter coefficients,it has a certain delay,usually has a slow convergence speed,and the system response time is long and easily affected by the learning rate leading to the lack of system stability,which often fails to achieve the desired control effect in practice.In this paper,we propose an active control algorithm with near-est-neighbor trap structure and neural network feedback mechanism to reduce the coefficient update time of the FxLMS algorithm and use the neural network feedback mechanism to realize the parameter update,which is called NNR-BPFxLMS algorithm.In the paper,the schematic diagram of the feedback control is given,and the performance of the algorithm is analyzed.Under various noise conditions,it is shown by simulation and experiment that the NNR-BPFxLMS algorithm has the following three advantages:in terms of performance,it has higher noise reduction under the same number of sampling points,i.e.,it has faster convergence speed,and by computer simulation and sound pipe experiment,for simple ideal line spectrum noise,compared with the convergence speed of NNR-BPFxLMS is improved by more than 95%compared with FxLMS algorithm,and the convergence speed of real noise is also improved by more than 70%.In terms of stability,NNR-BPFxLMS is insensitive to step size changes.In terms of tracking performance,its algorithm responds quickly to sudden changes in the noise spectrum and can cope with the complex control requirements of sudden changes in the noise spectrum.

Prediction Model of Drilling Costs for Ultra-Deep Wells Based on GA-BP Neural Network

Drilling costs of ultra-deepwell is the significant part of development investment,and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost.In order to improve the prediction accuracy of ultra-deep well drilling costs,the item and the dominant factors of drilling costs in Tarim oilfield are analyzed.Then,those factors of drilling costs are separated into categorical variables and numerous variables.Finally,a BP neural networkmodel with drilling costs as the output is established,and hyper-parameters(initial weights and bias)of the BP neural network is optimized by genetic algorithm(GA).Through training and validation of themodel,a reliable prediction model of ultra-deep well drilling costs is achieved.The average relative error between prediction and actual values is 3.26%.Compared with other models,the root mean square error is reduced by 25.38%.The prediction results of the proposed model are reliable,and the model is efficient,which can provide supporting for the drilling costs control and budget planning of ultra-deep wells.

基于ADASYN数据平衡化的PSO-BPNN变压器套管故障诊断

变压器套管作为设备重要的绝缘部件,其绝缘性能直接影响着设备的安全运行。为诊断变压器套管绝缘状态,改善变压器套管油中溶解气体的小样本不平衡数据对变压器套管故障诊断结果的影响,使用粒子群优化结合反向传播神经网络(particle swarm optimization combined with back propagation neural network,PSO-BPNN)和自适应综合过采样(adaptive synthetic sampling,ADASYN)算法对变压器套管进行故障诊断。首先收集变压器套管的历史故障数据,建立具有明确故障类别的变压器套管油中溶解气体样本集,并通过ADASYN算法对原始数据中的少数类样本进行合成,得到平衡后的故障数据,然后将平衡后的油中溶解气体作为模型输入,故障状态作为标签输出,通过PSO-BPNN模型对变压器套管进行诊断,最后在原始样本集下使用反向传播神经网络(back propagation neural network,BPNN)、遗传结合反向传播神经网络(genetic combined with back propagation neural network,G-BPNN)算法、布谷鸟搜索结合反向传播神经网络(cuckoo search combined with back propagation neural network,CS-BPNN)算法以及PSO-BPNN模型对套管进行诊断。结果表明,针对变压器油纸套管绝缘状态进行故障诊断的多个模型中,基于ADASYN平衡数据后的PSO-BPNN模型和其他模型相比准确度最高,能有效减小小样本不平衡数据对诊断结果的影响,为判断变压器油纸套管绝缘性能提供了有效方法。

基于改进EMD和GA-BPNN的机器人磨削颤振监测

由于工业机器人的灵活性,被广泛应用于机器人焊缝磨削任务中。但由于机器人的弱刚性,在焊缝磨削过程中系统容易发生颤振,因此对加工过程中的颤振监测是保证加工质量的基础。针对在加工振动信号处理过程中的模态混叠现象,提出了一种基于排列熵算法改进的经验模态分解方法,通过排列熵算法检测振动信号中的异常信号并剔除。通过相关系数法提取相关性最大的固有模态函数的能量熵作为特征值,同时提取方差、峰峰值、均方根和峭度4种时域特征。利用遗传算法优化BP神经网络(back propagation neural network,BPNN)建立颤振辨识模型,最后将提取的5种特征参数作为特征向量代入辨识模型中对加工状态进行监测。试验结果显示,提出的改进经验模态分解算法结合遗传算法优化的BPNN模型能够有效地对机器人焊缝磨削中的颤振进行监测。

基于SBAS-InSAR和BPNN的铀尾矿坝形变智能监测与预测

为提高铀尾矿库退役治理的监测工作效率,提出一个基于小基线合成孔径雷达干涉测量(SBAS-InSAR)技术和反向传播神经网络(BPNN)的铀尾矿库形变智能监测与预测模型。首先,利用SBAS-InSAR技术得到铀尾矿库2020年12月—2022年12月的累计形变量与年均形变速率,并用第一拦水坝的7个全球导航卫星系统(GNSS)监测站验证InSAR监测值的精度;然后,选取铀尾矿库中的雷公塘坝、南坡横坝、战斗坝和松林坝4个坝段的累计沉降量并结合降雨量进行沉降分析;最后,随机提取铀尾矿坝100个沉降点的累积沉降数据,通过BPNN预测铀尾矿坝的形变。结果表明:2年间铀尾矿库的形变速率在-60.06~34.94 mm/a,铀尾矿坝整体处于下沉状态,累计沉降量最大为-46.67 mm。BPNN预测值与实际监测值的平均绝对误差为0.586 mm,均方误差为0.624 mm。