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.

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.

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.

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.

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.

正交实验结合AHP和GA-BP神经网络优化益黄散醇提工艺

目的 优化益黄散的醇提工艺。方法 采用回流提取法,以乙醇体积分数、液料比、提取时间为考察因素设计正交实验,以橙皮苷、川陈皮素、橘皮素、没食子酸、诃黎勒酸、诃子酸、甘草苷、甘草酸、丁香酚含量和干浸膏得率为指标,采用层次分析法(AHP)进行赋权并计算综合评分。通过验证正交实验和遗传算法(GA)-反向传播神经网络(BP神经网络)所预测的结果确定益黄散最佳醇提工艺参数。结果 正交实验优选的最佳醇提工艺参数为乙醇体积分数60%、液料比14∶1(mL/g)、提取时间90 min、提取2次,验证所得综合评分为79.19分;GA-BP神经网络优选的最佳醇提工艺参数为乙醇体积分数65%、液料比14∶1(mL/g)、提取时间60 min、提取2次,验证所得综合评分为85.30分,高于正交实验所得结果。结论 采用正交实验结合GA-BP神经网络的寻优方法较传统的正交实验寻优方法效果更佳,其优选出的益黄散最佳醇提工艺稳定可靠。

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BP神经网络的高校教师精准教学能力评价模型构建

通过精准教学以促进学生个性化成长是教育理想和国家政策的不懈追求.高校教师是实施精准教学的“基”,现有关于其教学能力的评价体系中普遍存在概念不清和多采用主观构建评价指标的问题.为此,开展了基于BP神经网络的高校教师精准教学能力评价模型研究.首先,以理论研究为基础,对精准教学能力进行等级划分并构建评价指标框架,运用层级分析法建立指标权重;其次,利用BP神经网络智能学习的特性,以不同数据类型的指标值为输入,对应能力综合值为输出,检验精准教学能力分级及指标权重的合理性,进而生成较为客观的评价模型;最后,利用开发的评价系统和调查问卷进行样本数据采集和模型检验,从神经网络对数据的分类、拟合及仿真结果来看,模型能够对高校教师的精准教学能力进行客观评价,教师对模型测量结果的准确性也具有较高认可度.

基于改进BP神经网络的河北省碳排放预测

“双碳”目标背景下,针对河北省高碳经济发展模式难以改变、以往预测模型难以满足现实需求等问题。论文根据统计年鉴数据,研究河北省能源消费趋势和分行业碳排放特征,并借助脱钩指数探究河北省碳排放动态变化趋势,选取IPCC二氧化碳排放的计算方法,基于6项碳排放量影响因素建立遗传算法(GA)优化BP神经网络的河北省碳排放模型,对河北省2021—2030年碳排放量进行仿真预测。结果显示:河北省能源效率低于全国水平,河北省工业碳排放量最高;河北省的经济增长与碳排放之间主要呈弱脱钩态势;GA-BP模型预测结果比BP模型更加稳定,误差较小,更适合用于碳排放量的预测。预测结果显示,河北省未来碳排放量呈缓慢增长趋势,以期为政府决策提供理论依据,助力河北省“双碳”目标的实现。

基于BP神经网络的重力仪高机动状态快速调平修正技术

针对平台式重力仪大机动状态后测量能力恢复慢的问题,提出一种基于BP神经网络的重力仪稳定平台快速调平修正技术。首先,针对动态重力测量在测量平台大机动状态后调平能力不足的问题,研究了基于BP神经网络的平台姿态高效、准确解算方法;其次,利用惯性元件和卫星导航系统(GNSS)的信息优化BP神经网络,形成不同条件的平台姿态提取优化模型;最后,利用模拟仿真实验和实际机载动态重力测量数据验证所提方法的有效性和准确性。实验结果表明在大机动的动态条件下采用所提方法可以扶正重力仪稳定平台,将机动后重力仪稳定平台稳定时间缩短83.3%以上,提升动态重力测量效率。

基于AE-BP模型的杨木胶合板应力损伤识别

【目的】利用声发射(AE)技术对应力损伤全过程中的杨木胶合板进行无损检测,并利用BP神经网络对AE检测结果进行识别,以提高胶合板损伤检测精度。【方法】以市场占有量较高的托盘用杨木胶合板作为研究对象,在联合AE和应力损伤试验过程中,提取6个AE特征参数,利用声发射RA-AF联合分析法区分杨木胶合板产生裂纹的类型,采用K-均值聚类分析方法确定损伤演化程度和AE特征参数之间的对应关系,利用BP神经网络建立损伤识别模型,并对识别网络进行测试训练。【结果】AE信号幅度和上升时间可有效地表征杨木胶合板应力损伤从微裂纹萌生、产生宏观裂纹至完全断裂的损伤演化过程;通过RA-AF联合分析发现:杨木胶合板在应力损伤试验第一阶段主要为剪切破坏损伤,第二、三阶段主要为拉伸破坏损伤;通过K-均值聚类分析发现损伤类型与AE峰值频率之间的存在较强对应关系,可有效的表征不同的损伤类型:在31 kHz内为基体开裂,在31~100 kHz内为脱胶分层,大于100 kHz为纤维断裂;构建AE-BP神经网络模型对应力损伤类型训练样本的拟合优度是95.94%,测试集的拟合优度是98.89%,模型总拟合优度是96.51%,网络训练效果较好。【结论】在应力承载AE监测过程中,通过构建AE-BP模型,可对杨木胶合板产生的未知损伤进行有效检测并准确识别。

基于MPSO-BP算法的四电极电化学气体传感器温度补偿研究

针对四电极电化学气体传感器的测量精度极易受环境温度影响的问题,提出一种基于粒子群优化BP神经网络算法(PSO-BP)的温度补偿方法。利用改进的PSO算法(MPSO)对BP神经网络的权值和阈值进行优化,构造四电极电化学气体传感器的温度补偿模型,并设计了气体传感器测试系统。实验结果表明,MPSO-BP算法可有效提高BP神经网络的收敛速度和泛化能力;基于MPSO-BP算法的四电极气体传感器温度补偿模型,可将温度补偿误差控制在0.1%以内。

基于小波变换和GA-BP神经网络的电力电缆故障定位

由于电力电缆敷设于地下,当发生故障时难以快速且准确定位,出现了故障定位问题。因此,提出一种基于小波变换和遗传算法反向传播(Genetic algorithm back propagation,GA-BP)神经网络的电力电缆故障定位方法,在分析对比各小波能量集中程度和波动次数的基础上,选择多贝西小波(Daubechies wavelet 6,Db6)作为小波基函数,对于各故障位置,采集正向故障行波的α模分量,并对其进行小波分解。选取在d1尺度下的模极大值点作为特征值,同时将故障距离作为标签值,从而构建了训练和测试样本数据集;利用遗传算法(Genetic algorithm,GA)的种群进化和全局最优搜寻能力来改善误差逆传播(Back propagation,BP)网络对初始权重敏感的缺点,并使用优化后的权值、阈值重新对BP神经网络进行训练和预测,最后通过与传统双端行波定位算法、BP算法、粒子群优化BP算法(Particle swarm optimization BP,PSO-BP)相比较,证明了所提方法在测距性能方面的优越性。

基于FA-BP神经网络的生姜干燥含水率预测

为探索生姜的干燥特性,并实现生姜干燥的含水率预测,研究了不同干燥温度(50、55、60℃)、干燥风速(1.0、2.0、3.0m/s)、切片长度(30、35、40mm)对生姜干燥时间和干燥速率的影响。结合BP神经网络自适应能力、泛化能力、学习能力强和萤火虫算法(FA)参数少、寻优能力强、收敛速度快等特点,将干燥温度、干燥风速、切片长度和干燥时间作为输入层,隐藏层个数为10,输出层为生姜的含水率,搭建一个拓扑结构为“4-10-1”的FA-BP神经网络模型。研究结果表明:干燥温度、干燥风速、切片长度都是影响生姜含水率的关键因素,增加干燥风速、提高干燥温度和减少切片长度能有效缩短生姜的干燥时间,提高干燥效率。选用萤火虫算法优化BP神经网络的权值和阈值,减少了神经网络的训练时间,提高了精准度,其含水率预测值与试验值之间的决定系数R2=0.999 02,均方根误差RMSE为0.002 99,含水率预测结果准确且迅速,能够为生姜干燥过程中的含水率在线预测提供科学依据。

基于BP神经网络的集中供热二次网回水温度预测控制研究

针对集中供热系统二次管网存在的水力失调问题,设计了二次网水力平衡调节及回水温度预测模型,并实施智能控制策略,以实现二次网回水温度的精准控制。首先,构建BP神经网络预测模型,将此模型的输出视为二次网回水温度给定值;其次,在整个系统控制中,实施BP神经网络与PID控制器相结合的策略,进行二次网回水温度的控制。以高邑县某小区换热站数据为基础,通过阶跃响应曲线法建立二次网回水温度控制系统的数学模型,并通过BP-PID控制进行仿真实验。实验结果表明,与传统PID控制器相比,BP-PID控制器具有调节时间短、超调量小的优点,能够快速达到平稳状态。

基于BP神经网络的高桩码头基桩损伤识别研究

针对高桩码头基桩的损伤识别问题,基于BP神经网络开展了损伤定位研究。传统损伤定位方法在识别过程中受到人为主观因素的干扰,且对于只有一阶模态数据的情况定位效果有限。为克服这些问题,构建了不受人为因素影响的损伤定位神经网络,以第三类损伤指标ULSC和δFC作为训练样本,实现了对基桩局部损伤的准确定位。在建立合理的高桩码头有限元模型的基础上,构建了基于BP神经网络的损伤定位模型,并使用ABAQUS模拟数据和实测振动信号数据进行训练和测试。实验结果表明,该神经网络模型具有较高的定位准确性和鲁棒性,在不同损伤工况和10%噪声水平下仍表现优异。

基于SOM-BP的全自动口罩机传动系统故障检测

针对口罩机在多工序生产中故障特征难以诊断的问题,提出了一种基于自组织映射(SOM)和误差反向传播网络(BP)的故障检测模型。首先针对4种减速机故障类型搭建SOM-BP复合型神经网络模型并完成检测分类,其次通过提取原振动信号的20组时域和频域参数作为SOM网络的输入样本进行初步聚类,并根据仿真结果确定最佳竞争层结构,最后将聚类后结果输入BP网络进行预测并完成分类,实现故障检测。研究结果表明,7×7竞争层结构下的SOM-BP复合型神经网络对于减速机的8种时域和频域参数的检测效果最优,分类准确率可达93.5%,173次迭代即可收敛,数据拟合度最高达0.99876,达到实际检测要求,验证了该方案的有效性和可行性。

基于PCA-BP神经网络的巷道通风摩擦阻力系数预测模型

根据实测巷道通风摩擦阻力系数数据的特点,建立了主成分分析PCA-BP神经网络预测模型。采用PCA法对影响巷道通风摩擦阻力系数的支护类型、断面形状、巷道宽、巷道高、支护部分周边长、巷道断面积和巷道长度7个因素进行降维。将降维后因素的贡献率进行排序筛选,得到3个主成分指标(F_(1)、F_(2)和F_(3)),作为BP神经网络输入层的神经元。利用实测数据对PCA-BP神经网络模型进行训练和测试,并将测试结果与支持向量机回归(SVM)模型和BP神经网络模型的测试结果进行对比,结果显示:全因素的BP神经网络预测模型和SVM预测模型的平均精度分别为92.9420%、93.0235%,而PCA-BP预测模型的平均精度达到了96.4325%。PCA-BP神经网络模型不但简化了网络结构,更提高了网络的泛化能力,使预测误差更小、精度更高,为更准确地获得巷道通风摩擦阻力系数提供了一种有效的方法。

基于LDA-BP神经网络的高校思政课教师数据驱动决策力评价研究

数据驱动决策力为高校思政课教师提供科学合理的教学判断,对数据驱动决策力进行评价研究,有助于提高高校思政课教师的数据决策水平,进而提升思想政治教育教学质量。鉴于传统评测方法缺乏客观性与可重复性,运用LDA-BP神经网络技术构建高校思政课数据驱动决策力的指标体系与评价模型。首先,运用LDA方法对高校思想政治教育相关的政策文本与研究文献进行主题提取,并将主题信息作为指标构建基础;其次,通过研读文献与政策文本,并结合主题分析结果构建高校思政课教师数据驱动决策力评价指标体系;最后,通过对BP神经网络的训练及测试来生成高校思政课教师数据驱动决策力的评价模型。研究表明,高校思政课教师的专业知识、教学水平以及数据分析与解读能力是影响数据驱动决策能力的关键因素,据此,理应从素养提升、文化培育、管理革新、政府支持等方面入手增强数据驱动决策力。