FORCE RIPPLE SUPPRESSION TECHNOLOGY FOR LINEAR MOTORS BASED ON BACK PROPAGATION NEURAL NETWORK

Various force disturbances influence the thrust force of linear motors when a linear motor （LM） is running. Among all of force disturbances, the force ripple is the dominant while a linear motor runs in low speed. In order to suppress the force ripple, back propagation（BP） neural network is proposed to learn the function of the force ripple of linear motors, and the acquisition method of training samples is proposed based on a disturbance observer. An off-line BP neural network is used mainly because of its high running efficiency and the real-time requirement of the servo control system of a linear motor. By using the function, the force ripple is on-line compensated according to the position of the LM. The experimental results show that the force ripple is effectively suppressed by the compensation of the BP neural network.

Surface Quality Evaluation of Fluff Fabric Based on Particle Swarm Optimization Back Propagation Neural Network

Aiming at the problem that back propagation(BP)neural network predicts the low accuracy rate of fluff fabric after fluffing process,a BP neural network model optimized by particle swarm optimization(PSO)algorithm is proposed.The sliced image is obtained by the principle of light-cutting imaging.The fluffy region of the adaptive image segmentation is extracted by the Freeman chain code principle.The upper edge coordinate information of the fabric is subjected to one-dimensional discrete wavelet decomposition to obtain high frequency information and low frequency information.After comparison and analysis,the BP neural network was trained by high frequency information,and the PSO algorithm was used to optimize the BP neural network.The optimized BP neural network has better weights and thresholds.The experimental results show that the accuracy of the optimized BP neural network after applying high-frequency information training is 97.96%,which is 3.79%higher than that of the unoptimized BP neural network,and has higher detection accuracy.

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.

一种LM-BP加速搜索的周跳探测与修复方法

针对传统三频周跳探测与修复方法中的不敏感、漏检以及效率较低等问题,提出一种基于莱文伯格-马夸特(LM)-反向传播(BP)神经网络加速搜索法的伪距相位组合与电离层残差组合联合周跳探测与修复方法:利用2个伪距相位组合以减少不敏感周跳数量,利用1个电离层残差组合以提高小周跳探测敏感度;在构成3个线性无关的组合观测值后,使用LM-BP加速搜索算法进行周跳探测与修复。实验结果表明,相对常规的伪距相位组合与电离层残差组合联合方法,该方法能够提高周跳探测与修复性能,可探测小至1个的周跳,探测与修复整体时效有较大提升。

Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables

Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train,test and build a three-layer BP artificial neural network with multi-input and single-output.Ten water quality parameters were used to forecast phytoplankton biomass(measured as chlorophyll-a concentration).Correlation coefficient between biomass values predicted by the model and those observed was 0.964,whilst the average relative error of the network was-3.46% and average absolute error was 10.53%.The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass.A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass.Indicators were classified according to the sensitivity of response and its risk degree.The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH,sea surface temperature,sea surface salinity,chemical oxygen demand and ammonium.

基于BP神经网络的谐波减速器柔轮疲劳寿命预测研究

柔轮是谐波减速器的易损零件,在波发生器的高转速带动下转动,其疲劳寿命一直是备受关注的研究重点。以某型号杯型谐波减速器柔轮为研究对象,建立有限元仿真模型,得到柔轮最大应力与筒长、筒体壁厚和不同过渡圆角半径等参数之间的关系。根据柔轮S-N曲线,计算得到柔轮疲劳寿命,利用反向传播(Back Propagation,BP)神经网络实现了柔轮疲劳寿命的预测。

基于EMD-AVOA-BP的逆变器故障诊断方法

以CRH3C型动车组逆变器中的绝缘栅双极型晶体管(IGBT,Insulated Gate Bipolar Transistor)双管开路故障为研究对象,提出了一种基于非洲秃鹫算法(AVOA,African Vultures Optimization Algorithm)和优化的反向传播(BP,Back Propagation)神经网络的逆变器故障诊断方法。在Simulink中搭建列车逆变器的控制模型,取得故障电流;采用经验模态分解(EMD, Empirical Mode Decomposition)对电流信号进行去噪和故障特征提取,再利用AVOA对BP神经网络进行优化,实现了对列车逆变器IGBT双管开路故障的诊断。与传统方法进行对比可知,该方法具有更高的精准度,在测试集中其精准度达到100%。

基于GRU-BP算法的高精度动态物流称重系统

针对动态物流秤测量精度对载重、采样频率、带速较为敏感的问题,提出了一种高精度动态物流称重系统。首先,采用三因素五水平正交试验法,结合皮尔逊相关性检验原则,使用低通巴特沃斯与卡尔曼滤波器对传感器压力信号进行了滤波降噪处理,并将加速度信号作为模型输入信号,进行了特征补偿;然后,基于深度学习算法,提出了一种改进的门控循环单元模型,在该模型采样区间内将压力与振动改写为时序化信号,并将其共同输入门控循环单元(GRU)模型;最后,对GRU模型进行了改进,对其结构输出了层堆叠误差反向传播神经网络(BP),有效加强了模型的非线性映射能力。研究结果表明:在各类传动速度及测试货物下,该模型的最大测量误差相对于同类型深度学习模型长短期记忆(LSTM)神经网络、循环神经网络(RNN)时序模型及传统数值平均模型的误差,依次降低了16.14%、27.14%、76%,可用于各类称重系统。

Preparation of ZrB_2-SiC Powders via Carbothermal Reduction of Zircon and Prediction of Product Composition by Back-Propagation Artificial Neural Network

Phase pure ZrB2-SiC composite powders were prepared after 1 450℃/3 h via carbothermal reduction route,by using ZrSiO4,B2O3 and carbon as the raw materials.The influences of firing temperature as well as the type and amount of additive on the phase composition of final products were detailedly investigated.The results indicated that the onset formation temperature of ZrB2-SiC was reduced to 1 400℃by the present conditions,and oxide additive（including CoSO4·7H2O,Y2O3 and TiO2）was effective in enhancing the decomposition of raw ZrSiO4,therefore accelerating the synthesis of ZrB2-SiC.Moreover,microstructural observation showed that the as-prepared ZrB2 and SiC respectively had well-defined hexagonal columnar and fibrous morphology.Furthermore,the methodology of back-propagation artificial neural networks（BP-ANNs）was adopted to establish a model for predicting the reaction extent（e g,the content of ZrB2-SiC in final product）in terms of various processing conditions.The results predicted by the as-established BP-ANNs model matched well with that of testing experiment（with a mean square error in 10^（-3） degree）,verifying good effectiveness of the proposed strategy.

A Method for Solving Computer-Aided Product Design Optimization Problem Based on Back Propagation Neural Network

Because of the powerful mapping ability, back propagation neural network (BP-NN) has been employed in computer-aided product design (CAPD) to establish the property prediction model. The backward problem in CAPD is to search for the appropriate structure or composition of the product with desired property, which is an optimization problem. In this paper, a global optimization method of using the a BB algorithm to solve the backward problem is presented. In particular, a convex lower bounding function is constructed for the objective function formulated with BP-NN model, and the calculation of the key parameter a is implemented by recurring to the interval Hessian matrix of the objective function. Two case studies involving the design of dopamine β-hydroxylase (DβH) inhibitors and linear low density polyethylene (LLDPE) nano composites are investigated using the proposed method.

基于GA-BP神经网络的船舶空冷器状态预测

当前船舶空冷器的工作状态主要依靠空冷器冷却后的增压空气温度T_(A2)判断,通常按设定阈值触发报警,存在预警差和精度低等问题。对此,提出一种基于GA-BP(Genetic Algorithm-Back Propagation)神经网络的T_(A2)预测方法。利用BP神经网络构建空冷器状态预测模型,通过对比运行过程中T_(A2)实测值与预测值的偏差,及时发现空冷器的异常状态;引入GA解决BP神经网络存在的收敛速度慢和易于陷入局部最优解等问题。为验证基于GA-BP神经网络的预测方法的有效性,选取多组空冷器清洗前后的状态数据进行训练和验证,结果表明该方法能有效识别空冷器的异常状态。

基于BP神经网络的上海生鲜农产品物流需求预测

针对传统的生鲜农产品物流非线性需求预测模型收敛速度慢、精度低等问题,构建由改进粒子群(improved particle swarm optimization,IPSO)算法优化反向传播(back propagation,BP)神经网络的预测模型。引入对立学习机制、自适应惯性权重、非对称学习因子提升粒子群(particle swarm optimization,PSO)算法的初始解质量,平衡算法的局部开发和全局搜索能力;利用IPSO算法优化BP神经网络的权值和阈值,解决BP神经网络收敛速度慢、容易陷入局部最优等问题。通过上海生鲜农产品物流需求预测实例对模型的有效性进行验证,结果显示:IPSO-BP神经网络模型在预测精度及收敛速度上均明显优于传统PSO-BP神经网络和BP神经网络模型。

基于BP神经网络的转辙机故障检测方法

为提高城市轨道交通中ZDJ9型转辙机故障维修效率,提出基于反向传播(BP,Back Propagation)神经网络的转辙机故障检测方法。文章深入分析转辙机动作电流采集原理及现场转辙机转换过程中不同阶段电流曲线特征,确定故障电流曲线种类;对转辙机转换过程中动作电流曲线进行小波分解与重构,对重构后的曲线进行关键特征值提取,将其作为基于BP神经网络的故障检测模型训练数据,最终经过8 000次迭代训练后,故障检测模型的故障检测准确率达到96%,表明该方法能够有效检测转辙机故障及其故障类型。

基于GA-BP神经网络的风电功率预测方法研究

为了解决风电功率预测易受各种因素影响产生异常数据导致预测准确度不高的问题,提出了一种基于遗传算法-反向传播(GA-BP)神经网络的风电功率预测方法。首先,通过数学模型中的四分位算法对异常数据进行识别,并通过加入带通滤波器剔除异常数据。然后,在风电功率预测的方法上设计新型GA-BP神经网络算法,通过自检验及循环检测的方式获得准确的风电功率预测结果。试验结果表明,该方法不仅有很强的异常数据识别能力,而且在进行风电功率预测时可以保持90%以上的准确率,具有良好的数据处理稳定性。该研究大幅提升了风电功率预测的工作效率,为风电功率预测技术的进一步发展提供了技术参考。

基于BP神经网络和模糊隶属度的PM_(2.5)浓度校准

基于大量数据,采用Pearson相关系数与模糊隶属度分析自建点与国控点的细颗粒物(PM_(2.5))浓度数据相关性。其间通过建立反向传播(Back Propagation,BP)神经网络模型进行训练,并采用遍历试错法确定神经网络的最优算法与相关参数。经反复调试,校准结果相对于国控点数据的均方误差下降到0.005,均等系数为0.95,系统显示出优异的校准性能。研究结果表明,结合模糊隶属度预处理原始数据后,训练算法选用适宜、结构设定合理的BP神经网络能很好地校准自建点PM_(2.5)浓度数据,提高自建点数据精度。

基于BP神经网络算法构建糖尿病早期肾病风险预测模型

目的探讨糖尿病早期肾病的相关危险因素,并基于BP神经网络算法构建其风险预测模型。方法回顾性分析永康某中医院2020年1月至2022年12月收治的1048例糖尿病患者,其中糖尿病肾病患者115例,占10.97%,并以此分为DKD组(糖尿病肾病组115例)和DM组(糖尿病组933例)。收集患者相关资料,采用倾向性评分匹配(PSM)排除混杂因素后按1∶1最邻近方法进行匹配。以单因素分析中具有统计学意义的指标,运用BP神经网络算法基于相关因素构建预测模型。以平均绝对值误差(MAE)进行模型效能分析,以受试者工作特征曲线(ROC)评估风险预测模型的预测价值,并进行外部验证,采用校准曲线评估模型一致性。结果混杂因素有性别、合并高血压、空腹血糖、尿酸,将建模集按1∶1比例以最邻近方法进行PSM排除混杂因素后,DKD组95例,DM组95例。单因素分析结果提示患者年龄、2型糖尿病、总胆固醇(TC)、尿蛋白排泄率、糖尿病病程、胱抑素C(Cys C)组间差异具有统计学意义(P<0.05)。预测精度从大到小依次为BP神经网络算法、决策树、支持向量机、逻辑回归。BP神经网络结果显示影响糖尿病早期肾病发生重要性的前4位因素依次为蛋白尿排泄率、年龄、糖尿病病程、Cys C。预测模型AUC为0.959(95%CI:0.917~1.000),约登指数0.867,对应的敏感度与特异性分别为0.867、1.000。外部验证AUC为0.958(95%CI:0.922~0.995),其敏感度与特异性分别为0.804、1.000,校准图中校准曲线贴近于标准曲线。结论基于机器学习法构建的以年龄、病程、尿蛋白排泄率、TC、Cys C、2型糖尿病为预测特征的BP神经网络算法模型对糖尿病早期肾病有较好的预测价值,可以把该模型临床应用于此类高风险人群的管理识别。

基于BP神经网络的电商企业库存需求预测

随着电子商务的迅猛发展,市场需求的不稳定性日益凸显,这使得很多企业难以准确设定库存量,容易导致大量库存资金被占用。要想降低库存成本,必须准确预测市场需求,并据此设定合理的库存。文章以H公司为例,利用反向传播(Back Propagation,BP)神经网络预测方法预测其产成品需求,使企业保持合理的库存,减少不必要的库存资金。

基于GA-BP神经网络的软土路基运营期沉降预测

为了实现高速公路软土路基沉降的准确预测,采用遗传算法(GA)优化BP神经网络,研究3种输入对预测结果精度的影响.选取时间t以及其15 d前的沉降量S_(t-15)和平均沉降速率v_(t-15)为影响因素,在t、t-S_(t-15)、t-S_(t-15)-v_(t-15)三种输入下,分别取某高速公路软土路基运营期实测沉降数据的前50%、80%为训练集,余下原始数据为测试集,重复训练10次后取平均值作为输出值.采用决定系数(R^(2))来判别模型拟合度,均方根误差(RMSE)和平均绝对百分比误差(MAPE)作为模型性能的评价指标.结果表明:3种输入的R^(2)均大于0.99;训练集占原始数据的比例为50%时,t-S_(t-15)输入的预测误差最小,RMSE为1.31 mm,MAPE为4.71%;训练集占原始数据的比例为80%时,t-S_(t-15)-v_(t-15)输入的预测误差最小,RMSE为0.29 mm,MAPE为1.00%.t、t-S_(t-15)、t-S_(t-15)-v_(t-15)三种输入都可对路基沉降进行预测,其中t-S_(t-15)-v_(t-15)输入下取实测沉降数据的80%作为训练集时预测结果最精确.

基于动量自适应学习率PSO-BP神经网络的钻速预测模型研究

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。

蚁群BP神经网络在云制造知识服务组合优化中的应用

为实现针对复杂零件在云制造环境下知识资源的服务组合优化,建立了以QoS服务需求为优化目标的服务组合优化模型。首先,利用灰色关联分析对制造资源进行预处理以减小知识资源搜索范围,降低搜索成本。其次,利用改进的蚁群算法对知识资源进行全局优化分析,以提高匹配速度。最后,利用蚁群BP神经网络算法对解空间中最优解进行再次优化以提高知识服务组合的学习效率和学习精度。实验结果表明,灰色关联分析、改进的蚁群算法以及BP神经网络的使用,可缩短知识服务的搜索时间,提高匹配精确度,有效解决知识服务组合优化问题。