Study on the Model of Excessive Staminate Catkin Thinning of Proterandrous Walnut Based on Quadratic Polynomial Regression Equation and BP Artificial Neural Network

The excessive staminate catkin thinning （emasculation） of proterandrous walnut is an important management measure for improving yield. To improve the excessive staminate catkin thinning efficiency, the model of quadratic polynomial regression equation and BP artificial neural network was developed. The effects of ethephon, gibberel in and mepiquat on shedding rate of staminate catkin of pro-terandrous walnut were investigated by modeling field test. Based on the modeling test results, the excessive staminate catkin thinning model of quadratic polynomial regression equation and BP artificial neural network was established, and it was validated by field test next year. The test data were divided into training set, vali-dation set and test set. The total 20 sets of data obtained from the modeling field test were randomly divided into training set （17） and validation set （3） by central composite design （quadric rotational regression test design）, and the data obtained from the next-year field test were divided into the test set. The topological struc-ture of BP artificial neural network was 3-5-1. The results showed that the pre-diction errors of BP neural network for samples from the validation set were 1.355 0%, 0.429 1% and 0.353 8%, respectively; the difference between the predicted value by the BP neural network and validated value by field test was 2.04%, and the difference between the predicted value by the regression equation and validated value by field test was 3.12%; the prediction accuracy of BP neural network was over 1.0% higher than that of regression equation. The effective combination of quadratic polynomial stepwise regression and BP artificial neural network wil not only help to determine the effect of independent parameter but also improve the prediction accuracy.

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.

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.

Path Planning and Tracking for Vehicle Parallel Parking Based on Preview BP Neural Network PID Controller

In order to diminish the impacts of extemal disturbance such as parking speed fluctuation and model un- certainty existing in steering kinematics, this paper presents a parallel path tracking method for vehicle based on pre- view back propagation （BP） neural network PID controller. The forward BP neural network can adjust the parameters of PID controller in real time. The preview time is optimized by considering path curvature, change in curvature and road boundaries. A fuzzy controller considering barriers and different road conditions is built to select the starting po- sition. In addition, a kind of path planning technology satisfying the requirement of obstacle avoidance is introduced. In order to solve the problem of discontinuous curvature, cubic B spline curve is used for curve fitting. The simulation results and real vehicle tests validate the effectiveness of the proposed path planning and tracking methods.

Intelligent direct analysis of physical and mechanical parameters of tunnel surrounding rock based on adaptive immunity algorithm and BP neural network

Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relationships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data. This is of practical significance to the appraisal of stability and informationization design of the surrounding rock.

A Prediction Method of Charging Station Planning Based on BP Neural Network

The construction of charging service facilities is a very important factor in the popularization of electric vehicles. Therefore, the planning problems of electric vehicle charging station are urgent to be solved. Considering the standard of natural environment, society, traffic, power grid and economy, an evaluation system is created for electric vehicle charging station project through 15 sub-standards. Planning model of charging station is constructed based on BP neural network adopted in the analysis. It is used for location and capacity prediction of charging station planning. By analyzing the model with data samples, a stable network structure is established and the feasibility of the model is verified in the charging station planning.

An Evaluating Model for Enterprise's Innovation Capability Based on BP Neural Network

To meet the challenge of knowledge-based economy in the 21st century,scientifically evaluating the innovation capability is important to strengthen the international competence and acquire long-term competitive advantage for Chinese enterprises.In the article,based on the description of concept and structure of enterprise's innovation capability,the evaluation index system of innovation capability is established according to Analytic Hierarchy Process(AHP).In succession,evaluation model based on Back Propagation(BP) neural network is put forward,which provides some theoretic guidance to scientifically evaluating the innovation capability of Chinese enterprises.

Daily ETC Traffic Flow Time Series Prediction Based on k-NN and BP Neural Network

Daily Electronic Toll Collection(ETC)traffic flow prediction is one of the fundamental processes in ETC management.The precise prediction of traffic flow provides instructions for transportation hub management solution planning and ETC lane construction.At present,some of studies are proposed in forecasting traffic flow.However,most studies of model presentation are in the form of mathematical expressions,and it is difficult to describe the trend accurately.Therefore,an ETC traffic flow prediction model based on k nearest neighbor searching(k-NN)and Back Propagation(BP)neural network is proposed,which takes the effect of external factors like holiday,the free of highway and weather etc.into consideration.The traffic flow data of highway ETC lane somewhere is used for prediction.The prediction results indicate that the total average absolute relative error is 5.01%.The accuracy suggests its advantage in traffic flow prediction and on site application.

基于GRU-CNN双网络输出构建BP模型的径流预测方法

提高径流预测精度是避免洪水灾害发生的重要手段,由于预测阶段并无已知有效样本,给预测工作带来难度,因此,提出以双网络输出为预测阶段提供数据参考,结合训练阶段双网络输出与真实值之间的关系,对预测阶段采用二次多变量建模实现径流预测。首先,构建GRU和CNN深度学习网络,同步输出2条径流预测序列;其次,在已知时段内,构建2条预测结果与实测值之间的多变量BP模型;最后,基于双网络输出预测值,通过确定的BP模型输出径流预测结果。经测试,该方法给预测时段提供了可靠的先验样本,高效学习了网络输出与真实值之间关系,预测精度显著提升。

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。

Study on Remote Sensing of Water Depths Based on BP Artificial Neural Network

A momentum BP neural network model （MBPNNM） was constructed to retrieve the water depth information for the South Channel of the Yangtze River Estuary using the relationship between the reflectance derived from Landsat 7 satellite data and the water depth information. Results showed that MBPNNM, which exhibited a strong capability of nonlinear mapping, allowed the water depth information in the study area to be retrieved at a relatively high level of accuracy. Affected by the sediment concentration of water in the estuary, MBPNNM enabled the retrieval of water depth of less than 5 meters accurately. However, the accuracy was not ideal for the water depths of more than 10 meters.

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。

Quantitative Detection Model of Pernicious Gases in Pig House Based on BP Neural Network

To find a neural network model suitable to identify the concentration of mixed pernicious gases in pig house, the quantitative detection model of pernicious gases in pig house was set up based on BP （ Back propagation） neural network. The BP neural network was trained separately by the three functions, trainbr, traingdm and trainlm, in order to identify the concentration of mixed pernicious gases composed of ammonia gas and hepatic gas. The neural network toolbox in MATLAB software was used to simulate the detection. The results showed that the neural network trained by trainbr function has high average identification accuracy and faster detection speed, and it is also insensitive to noise; therefore, it is suitable to identify the concentration of pemidous gases in pig house. These data provide a reference for intelligent monitoring of pemicious gases in pigsty.

Prediction of Injection-Production Ratio with BP Neural Network

Injection of water to enhance oil production is commonplace, and improvements in understanding the process are economically important. This study examines predictive models of the injection-to-production ratio. Firstly, the error between the fitting and actual injection-production ratio is calculated with such methods as the injection-production ratio and water-oil ratio method, the material balance method, the multiple regression method, the gray theory GM (1,1) model and the back-propogation (BP) neural network method by computer applications in this paper. The relative average errors calculated are respectively 1.67%, 1.08%, 19.2%, 1.38% and 0.88%. Secondly, the reasons for the errors from different prediction methods are analyzed theoretically, indicating that the prediction precision of the BP neural network method is high, and that it has a better self-adaptability, so that it can reflect the internal relationship between the injection-production ratio and the influencing factors. Therefore, the BP neural network method is suitable to the prediction of injection-production ratio.

基于BP-ANN与RBF-ANN的钢筋与混凝土黏结强度预测模型研究

为研究神经网络对钢筋与混凝土黏结强度的预测能力以及神经网络的输出性能,基于大量的试验数据,提出一种基于改进神经网络的变形钢筋与混凝土黏结强度预测模型,对混凝土结构的研究与实际工程应用均有着重要的意义。收集290组黏结锚固试验数据,引入基于反向传播人工神经网络(BP-ANN)与径向基函数神经网络(RBF-ANN)算法,揭示混凝土强度、保护层厚度、钢筋直径、锚固长度及配箍率对变形钢筋与混凝土黏结性能的影响规律,建立基于改进神经网络算法的钢筋与混凝土黏结强度预测模型。对比分析不同数据预处理方法和训练神经元个数对建议模型预测结果的影响,评估各经典模型与建议模型的预测精度和离散性,提出临界锚固长度计算公式。结果表明:BP-ANN预测值与试验值比值的均值、标准差及变异系数分别为1.009、0.188、0.86,其预测精度略高于RBF-ANN;建议模型能够更准确、更稳定地预测钢筋与混凝土的黏结强度,该方法为解决钢筋与混凝土黏结问题提供了新思路。

Spatial Interpolation of Soil Nutrients Based on BP Neural Network

With Zengcheng City, Guangdong Province, as the object of study, 200 soil sampling points were col ected for the spatial interpolation prediction of soil properties by using Kriging method and BP neural network method. After comparing the interpolation results with the measured values, the root mean square error of the prediction data was obtained. The results showed that the interpolation accuracy of BP neural network was higher than that of Kriging method under the same cir-cumstances, and there was no smoothness in using BP neural network method when there were few sample points. In addition, with no requirement on the distri-bution of sample data, BP neural network method had stronger generalization ability than traditional interpolation method, which was an alternative interpolation method.

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.

基于改进FNN-BP网络的304不锈钢薄板焊接质量推断模型

针对目前激光焊接领域的激光焊接参数智能设定的发展方向,智能焊接系统的焊接参数推定模块成为了热点研究对象。在分析了焊接工艺参数对焊接质量的影响之后,搭建了一种基于改进模糊专家系统和BP神经网络的激光焊接质量推断模型,该模型包括两部分内容,即基于焊接速度、焊接功率和离焦量的焊接质量模糊推断和基于预测值、板材厚度、峰值功率和占空比的BP修正神经网络。焊接质量模糊推断,首先基于已有人工经验进行焊接参数模糊化和焊接规则库建立,然后通过分析确定模糊推断类型,最后进行模糊推断输出焊接质量预测值;BP神经网络修正,基于板材厚度等参数对不同板材厚度下焊缝图像质量评分和平面度差值进行预测值修正,以获得更加准确的推断值。通过实验证明,该不锈钢薄板智能激光焊接系统具有一定的可行性和重要的工程意义。

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。