Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network

For optimal design of mechanical clinching steel-aluminum joints, the back propagation （BP） neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm （GA） is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM Tog-L-Loc system with different technologic parameters. The training samples＇ parameters and the corresponding joints＇ mechanical properties are supplied to the artificial neural network （ANN） for training. The validating samples＇ experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model＇s prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.

Water quality forecast through application of BP neural network at Yuqiao reservoir

This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin. To overcome the shortcomings of traditional BP algorithm as being slow to converge and easy to reach extreme minimum value,the model adopts LM (Leven-berg-Marquardt) algorithm to achieve a higher speed and a lower error rate. When factors affecting the study object are identified,the reservoir's 2005 measured values are used as sample data to test the model. The number of neurons and the type of transfer functions in the hidden layer of the neural network are changed from time to time to achieve the best forecast results. Through simulation testing the model shows high efficiency in forecasting the water quality of the reservoir.

Prediction and Analysis of Air Quality Based on FCM and BP Neural Network

In the paper,we solve the problems of air quality prediction and evaluation. Firstly,the original data of air quality monitoring are classified by fuzzy C means clustering algorithm（ FCM）; then the BP neural network model to predict the level of air quality is built through the simulation training of data. Experiments show that the model has good generalization ability and strong stability,and the prediction accuracy is higher,which has certain application value.

Construction of Air Quality Evaluation System Based on FCM Algorithm and BP Neural Network

In order to solve the limitations of existing air quality evaluation system, a new air quality evaluation system was established based on FCM, the BP neural network, with the aim to provide scientific bases for the targeted and efficient control of air pollution, formulation of prevention and control strategy, and improvement of living environment. Based on the existing data of 6 air quality indices, the air quality data were reclassified by using FCM algorithm, obtaining the clustering center, which minimized the cost function of non-similar index. Then, the reclassified 6 classes of data were proceeded with BP neural network training and simulation, so as to achieve the purpose of identification, thereby forming a new air quality evaluation system.

Evaluation Method of Quality Chain Synergy Effects based on BP Neural Network

Aiming at the problem of the quality chain synergy effects evaluation, this paper constructed an index system of quality chain synergetic evaluation and established quality chain synergetic evaluation model of BP neural network. On the basis of training original data by BP neural network and processing the original sample data by the method of Grey Theory, the text achieved the measure of quality chain synergy effects. And the validity of the method is also verified by simulation analysis

大红袍花椒对流-辐射并联干燥BP神经网络模拟与品质研究

为探究热风干燥、热风-红外和热风-微波对流-辐射并联干燥对大红袍花椒干燥特性及品质的影响,实验分析了不同温度、装载量、干燥功率等条件下的大红袍花椒干燥曲线特征,运用BP神经网络进行拟合,并采用感官评价与GC-MS对3种干燥方式干制大红袍花椒挥发油进行分析。结果表明,热风-红外并联干燥的恒速期干燥速率高于热风干燥一个数量级,热风-微波并联干燥时长最短,出现二次升速且降速期不明显;升温、减少装载量或加大微波功率均有利于提高干燥速率和缩短干燥时间。BP神经网络的相关系数R值均能达到0.985以上,均方误差最低可达1.010×10^(-4),平均相对误差值E为4.55%,可很好地描述大红袍花椒的干燥动力学特性,预测大红袍花椒干燥过程含水率准确且迅速。3种干燥方式挥发油分别鉴定出40、39、28种化学成分,热风-微波并联干燥获取大红袍花椒挥发油中烯烃类化合物相对含量最大,但色泽和口感差,热风干燥花椒色泽最佳,挥发油中化合物种类最多,热风-红外并联干燥既缩短了大红袍花椒干燥时间,又能保持其良好品质,为较佳选择。

基于BP神经网络的寒区再生微粉工程水泥基复合材料力学性能研究

为了探究不同低温不利条件下再生微粉ECC材料力学性能的影响,本文采用控制变量法,以再生微粉种类和取代率为研究变量,探究了再生微粉ECC在冻融循环和恒低温两种低温不利条件下的抗压和抗折强度试验。分析了再生微粉种类、再生微粉取代率、冻融循环次数、恒低温温度对再生微粉ECC力学性能的影响。最后基于BP神经网络,建立了3-6-1的冻融循环和3-3-1的恒低温BP神经网络结构抗压强度预测模型。研究结果表明:在相同的冻融循环条件下,再生混凝土微粉ECC的力学性能要高于再生砖粉ECC,且均随再生微粉取代率的增加先小幅度下降后剧烈下降,在经历150冻融循环后力学性能损失20%左右。而经历恒低温保温后的再生微粉ECC力学性能呈现出相反的变化趋势,随着低温保温温度的降低再生微粉ECC的力学强度反而呈现上升趋势,从常温到-40℃恒低温状态下力学性能提高22%左右。建立的两个低温不利条件下BP神经网络预测模型,平均相对误差分别为1.43%、1.28%,并以质量损失率和相对动弹模量为评判标准,预测试验范围内不同配合比的再生微粉ECC可经受的最大冻融循环次数。

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。

基于回归分析和GA-BP神经网络算法的3D打印件弯曲性能预测

为进一步探究熔融沉积成型(FDM)3D打印参数和制件弯曲性能之间的关系,创建合理的FDM 3D打印制件弯曲强度预测模型。根据正交试验L_(16)(4^(5))的设计原则和神经网络算法模型的构建要求,按照不同分层高度、填充密度、打印温度、打印速度以及外壳厚度五种因素,制备25组试验试样,并进行弯曲性能检测。随后通过建立GA-BP神经网络模型、传统BP神经网络模型以及多元回归方程模型,分别对FDM 3D打印制件弯曲性能进行预测,并将预测数据与试验测试数据进行对比。通过对比发现,GA-BP神经网络模型预测数据与试验测试数据更为接近,其平均误差为3.71%,且误差值整体波动最小,BP神经网络模型与多元回归方程模型预测精度相差不大,BP神经网络模型预测平均误差为8.05%,多元回归方程模型预测平均误差为9.07%,但多元回归方程误差值整体波动最大。因此,采用GA遗传算法优化后的BP神经网络模型在进行FDM 3D打印制件弯曲性能预测方面具有更高的精度和更良好的稳定性。

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BP神经网络和遗传算法的智能配煤系统开发与应用

针对炼焦煤品种繁多,同一矿点来煤的煤质波动较大,混煤现象严重的问题,宁波钢铁有限公司通过搭建煤焦数据库,开发智能配煤系统,实现全流程监测煤焦数据变化。智能配煤系统结合历史生产数据分析提取影响焦炭质量的关键指标,采用多元线性回归和BP神经网络的建模方法,建立焦炭质量关键指标预测模型。同时,智能配煤系统结合焦炭质量预测模型、配煤专家系统和炼焦单种煤库存信息,采用优化后的遗传算法进行配煤模型的构建,从而实现快速实时调整配比、合理利用炼焦煤资源、稳定焦炭质量并且有效降低炼焦成本的目的。智能配煤系统运行稳定,实现了对炼焦煤资源的合理利用和降本增效的目的。

A Worsted Yarn Virtual Production System Based on BP Neural Network

Back-Propagation (BP) neural network and its modified algorithm are introduced. Two series of BP neural network models have been established to predict yarn properties and to deduce wool fiber qualities. The results from these two series of models have been compared with the measured values respectively, proving that the accuracy in both the prediction model and the deduction model is high. The experimental results and the corresponding analysis show that the BP neural network is an efficient technique for the quality prediction and has wide prospect in the application of worsted yarn production system.

基于BP神经网络的雪茄原料感官质量预测模型构建

本研究旨在利用BP神经网络技术,深入分析并预测雪茄原料的常规化学成分与其感官质量之间的复杂关系。通过收集四川、湖北、云南、湖南和尼加拉瓜雪茄烟叶常规化学成分数据作为输入变量,结合雪茄原料各项感官质量指标作为输出变量,成功构建了拓扑结构为6-9-1的BP神经网络模型。该模型不仅能够准确预测雪茄原料的感官质量评吸结果,而且揭示了不同产区雪茄烟叶在化学成分和感官质量方面的独特特征。研究表明,所检测样本中,国内4个主产区雪茄烟叶总糖、还原糖、烟碱、氯含量均高于尼加拉瓜烟叶,尼加拉瓜烟叶香气质和香气量得分较高。四川烟叶刺激性得分较低,湖北产区雪茄烟叶余味得分较高,云南烟叶杂气得分较低,湖南烟叶燃烧性和灰色得分较高。本研究雪茄烟叶样本的常规化学成分和感官质量指标统计特征较好,基本服从正态分布。所构建的BP神经网络模型的预测值与实际值间差异较小,其中余味、刺激性、灰色和总分的相关系数均在0.9以上。在训练集、验证集和测试集的预测值和实际值误差中,除总分误差区间较大外,剩余多数指标误差区间在0~0.5范围内的比例达到85%以上。BP神经网络所建立的雪茄原料感官质量预测模型拟合效果较好。本研究的成功实施为基于常规化学成分快速、准确地预测雪茄原料感官质量提供了有力支持,有助于推动中式雪茄烟行业的创新发展。

Modeling of mechanical properties of as-cast Mg-Li-Al alloys based on PSO-BP algorithm

Artificial neural networks have been widely used to predict the mechanical properties of alloys in material research. This study aims to investigate the implicit relationship between the compositions and mechanical properties of as-cast Mg-Li-AI alloys. Based on the experimental collection of the tensile strength and the elongation of representative Mg-Li-AI alloys, a momentum back-propagation （BP） neural network with a single hidden layer was established. Particle swarm optimization （PSO） was applied to optimize the BP model. In the neural network, the input variables were the contents of Mg, Li and AI, and the output variables were the tensile strength and the elongation. The results show that the proposed PSO-BP model can describe the quantitative relationship between the Mg-Li-AI alloy＇s composition and its mechanical properties. It is possible that the mechanical properties to be predicted without experiment by inputting the alloy composition into the trained network model. The prediction of the influence of AI addition on the mechanical properties of as-cast Mg-Li-AI alloys is consistent with the related research results.

基于BP神经网络的飞行员着舰训练品质评估

舰载战斗机在深海作战中发挥着重要作用,飞行员着舰训练品质的高低直接影响着舰载机的战斗力,以往对飞行员着舰训练品质的评估采用人工方式,很少尝试神经网络。针对这方面的不足,提出了基于反向传播(BP)神经网络的飞行员着舰训练品质评估方法。利用着舰飞行参数、舰载机尾钩挂锁情况以及专家组评分,构建数据集;对网络进行训练和测试,确定网络参数,训练着舰评估网络模型;通过验证集对网络进行仿真验证,验证模型的可靠性。验证结果表明,该网络能较为准确地评估着舰分数和尾钩挂锁情况,可为飞行员着舰训练提供参考。

基于改进SABO-BP算法的电网谐波预测

针对日趋严重的电网谐波污染亟需大量谐波数据支撑分析和治理及电网谐波监测能力不足的问题,提出一种改进减法平均优化(subtraction average based optimizer, SABO)算法优化反向传播(back-propagation, BP)神经网络实现谐波预测,以缓解当前谐波数据匮乏的问题。为了克服现有SABO算法易于陷入局部最优解,初始化时使用Logistic混沌映射替代随机数,同时迭代搜索中利用黄金正弦优化算法辅助SABO跳出局部最优,从而提高BP神经网络预测准确率。最后,以某省实际运行数据验证所提改进SABAO-BP模型在谐波电压畸变率及单次谐波电压含有率预测中均具有较高准确性。

基于BP神经网络的海底浅层超软黏土不排水抗剪强度预测

准确评估海底浅层超软黏土的不排水抗剪强度对海洋资源开发、灾害评估、海洋工程建设等具有重要的意义。世界多个海域超软黏土数据分析对比表明,现有不排水抗剪强度评估经验公式存在受地域限制的问题,计算精度较低。因此,以超软黏土的6个物性指标为输入变量,不排水抗剪强度为输出变量,建立BP神经网络模型进行海底浅层超软黏土不排水抗剪强度预测;根据建立的超软黏土不排水抗剪强度与物性指标之间的非线性映射网络,以权积法求解超软黏土不排水抗剪强度对各指标的敏感度系数,定量分析各指标对不排水抗剪强度的影响程度。结果表明:考虑多因素影响的BP神经网络模型在超软黏土不排水抗剪强度预测方面具有普遍适应性,其预测结果非常接近落锥试验实测结果,均方差0.02139,R2值达到0.9874,预测精度远高于现有经验公式;采用权积法计算得到的对数流动性指标对不排水抗剪强度最为敏感;据此建立了以对数流动性指标为参数的黏土不排水抗剪强度预测经验公式,该公式在计算中国海域超软黏土不排水剪切强度方面具有较高精度,但由于数据量有限,依旧存在一定局限性。本研究为海底浅层超软黏土不排水抗剪强度的计算提供了参考。

基于改进BP神经网络的装配质量预测方法

装配是产品制造过程中耗费大量时间和精力的重要环节,影响着产品整个生命周期。针对产品装配效率低的问题,提出遗传算法优化BP神经网络的装配质量预测方法。以DC零件的质心后端、质量、长度这三个质量特性为基础,划分数据,确定BP神经网络结构,以均方误差作为遗传算法的适应度函数,寻找最优的初始权值和阈值,建立了遗传算法优化BP神经网络模型,并结合平均绝对误差MAE、均方误差MSE、均方根误差RMSE对预测结果进行了对比。实验结果表明:相比于传统的BP神经网络,经过遗传算法优化的BP神经网络在质量预测方面具有更好的精度和准确性。

BP神经网络预测船用钢焊接接头力学性能研究

采用不同成分母材和焊丝进行焊接工艺试验,研究母材成分、焊材成分、热输入和焊接位置等参数对焊接接头力学性能的影响,为进一步提升模型预测精度,通过遗传算法对BP神经网络进行优化,将优化权值和阈值赋值给BP神经网络进行建模,预测结果表明,优化模型稳定性好,提高了预测的精度和泛化能力,为焊接接头力学性能预测提供了借鉴,对船用钢和焊材冶金成分设计具有借鉴意义。