BP神经网络补偿并联机器人定位误差

为减小机构末端定位误差,提高机器人运动精度,分析了所开发的6-DOF精密并联机器人末端位姿的误差来源及以往误差补偿方法的局限性。通过实际测量末端位姿,在精密定位的局部工作空间内,提出了基于BP神经网络的机器人关节空间误差补偿方法。确定了BP神经网络模型,建立了误差补偿的数据样本,并对数据样本进行了标准化,通过实验对比的方法确定了隐层神经元的个数,同时对网络的推广能力进行了验证。经过误差补偿,6-DOF精密并联机器人的平移定位误差下降了80%,转角定位误差下降了60%。该实验结果表明,基于BP神经网络的误差补偿方法对机器人局部工作空间的补偿具有明显的效果,满足精密并联机器人工作的精度要求。     

BP神经网络补偿热变形误差的研究

在精密加工中，由于热变形引起的误差占整个系统误差的40％－60％[1]，这说明对热变形进行深入研究和找出其规律并提出相应的补偿措施是十分必要的。本文是以CK616－1简易数控车床为实验对象，在对其热误差分析的基础上进行热误差建模，并结合改进的BP神经网络给出了具体实现的方法，对提高机床的加工精度有着极其重要的意义。     

基于遗传算法优化BP神经网络的数控机床热误差补偿

以提高数控机床加工精度为主要目的,针对减少热误差而提出一种基于遗传算法优化BP神经网络的数控机床热误差补偿方法.首先,分析遗传算法优化的BP神经网络学习算法.然后,建立神经网络模型对三轴联动卧式加工中心进行实时补偿.实验仿真结果表明遗传优化BP神经网络模型具有预测补偿能力强、补偿精度高、拟合性能优、实时性好等特点.     

基于BP神经网络的压力传感器误差补偿算法研究

采用BP神经网络来建立扩散硅压力传感器的输出输入模型,其网络模型具有三层结构,采用改进型的差分进化算法来优化BP神经网络的权值和阀值,并在MATLAB中进行了仿真。经训练得到补偿后扩散硅压力传感器的输出满量程误差可达到0．035％,结果表明采用基于改进型差分进化算法的BP神经网络建模对提高智能差压传感器的测量准确度具有参考价值。     

基于BP神经网络的五轴数控机床热误差补偿建模

随着我国经济实力的不断发展,我国对于工业加工领域的重视程度在不断的加强,尤其是在数控机床的应用,具有重要的意义,不仅能够有效提高数控机床工作效率和质量,还能保障产品质量。为了有效判断数控机床档次,应当以数控机床加工精度作为主要的检测标准,而传统的五轴数控机床在热影响下,经常性的出现加工精度下降的问题,针对于此,本文基于BP神经网络的五轴数控机床热误差补偿情况进行建模分析,对比之前的应用数据可知其能够有效提高五轴数控机床的加工精度,具有重要的推广价值。     

铣削加工误差的神经网络补偿技术

本文对影响铣削加工误差的信息进行分析,运用人工神经网络技术,介绍了在线误差补偿控制系统的建立原理,建立铣削加工误差信息的神经网络结构和误差补偿模型,并通过对样本的合理选择来提高补偿的能力,以提高加工精度。     

基于BP神经网络的车削加工刀具变形误差补偿技术

针对目前普通机床加工存在精度不足的问题,提出了一种精度误差补偿方法,使普通机床也能获得较高的加工精度。首先,在分析车床加工误差的基础上,引入了神经网络模型,从结构和方法上进行了详细阐述,并利用MATLAB构建;输入样本对网络进行训练,获取预测值,验证了本方案的正确性和可行性。     

BP神经网络补偿三维曲面非接触式测量系统热变形误差研究

针对一个三维非接触式测量系统，在简要介绍系统工作原理的基础上，指出机械部分的热变形误差是影响系统误差的关键。该文在分析测量系统机械部分的基础上对它进行了热误差的分析和建模，并结合改进的BP神经网络给出了具体实现的方法。     

神经网络在激光位移传感器误差补偿中的应用

为降低入射角对激光定位移传感器测量精度的影响,提出一种新型的误差补偿方法：先采用ＢＰ神经网络实现测量误差与法矢章动角和进动补偿方法可有效地提高激光位移传感器的测量精度。在此基础上,提一步提出了自由工面测量误差的补偿方法。     

基于BP神经网络的传感器特性补偿新算法的研究

在用人工神经网络对传感器特性进行补偿的基础上,进行了一些改进与简化,提出了一种简化的快速算法,通过多步继承法、神经元功能函数平移法、停止条件比较法等措施,对BP神经网络本身的一些缺陷,如收敛速度慢、容易收敛到局部最小点等进行了弥补,并用MATLAB语言编制了训练程序。结果表明,该算法可以进一步提高数据拟合的精度,而且大幅度地节省了时间。     

基于前馈神经网络的汽车覆盖件模具报价系统

传统的模具报价方法已难以满足现代市场对高精度、高效率报价的需求.为此,提出了基于前馈神经网络的覆盖件模具报价方法.建立了兼有前馈神经网络法、重量法和基点工时法,可支持全流程报价的覆盖件模具报价方法体系;开发出支持产业链协作的覆盖件模具报价系统,并实现它与汽车模具产业链协作公共服务平台的集成,可对不同报价阶段、不同产业链应用对象提供支持.通过实例,验证了馈神经网络报价法的适用性、合理性和有效性.     

基于改进型BP神经网络的氢原子钟钟差预测

原子钟的钟差预测是原子钟时标计算和原子钟驾驭的关键环节,良好的钟差预测可明显提高原子钟时标和原子钟驾驭的精度。为进一步提高氢原子钟的钟差预测精度,本文提出了一种改进型的BP神经网络算法,并用中国计量科学研究院守时实验室氢原子钟组的实际数据进行了验证。验证结果表明,本文提出的改进型BP神经网络钟差预测算法与目前采用的线性回归钟差预测算法、SVM钟差预测算法相比,显著地提高了氢原子钟钟差预测精度。该钟差预测算法的提出对提高原子钟时标和驾驭精度有很好的推动作用。     

Effect of different features to drill-wear prediction with back propagation neural network

In this paper, a back propagation neural network (BPNN) has been applied to predict the corner wear of a high speed steel (HSS) drill bit for drilling on different workpiece materials. Specially defined static and dynamic features extracted by a wavelet packet transform (WPT) from the resultant force converted from thrust and torque together with the cutting conditions (workpiece material, spindle speed, drill diameter, feed rate) are used as inputs to train the network to obtain a better output, drill corner wear. Drilling experiments have been carried out over a wide range and, features newly defined and conventional ones, features extracted from different frequency bands are compared.     

应用BP神经网络预测高速铣削表面粗糙度

表面粗糙度的预测是切削加工质量分析的重要研究方向,为了在保证铣削的同时预测加工表面的粗糙度、提高生产率,将人工神经网络技术应用于铣削加工领域。应用BP神经网络建立高速铣削加工表面粗糙度预测模型,将预报结果与试验真值进行对比验证,结果表明该方法能够得到较好的预测精度,对高速铣削参数的选择和表面质量的控制具有指导意义。     

Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods

This paper deals with a new scheme for the prediction of a ball bearing's remaining useful life based on self-organizing map (SOM) and back propagation neural network methods. One of the key components needed for effective bearing life prediction is the set-up of an appropriate degradation indicator from a bearing's incipient defect stage to its final failure. This new method is different from the others that have been used in the past, in that it uses the minimum quantisation error (MQE) indicator derived from SOM, which is trained by six vibration features, including a new designed degradation index for performance degradation assessment. Then, using this indicator, back propagation neural networks focusing on the degradation periods can be trained. Thanks to weight application to failure times (WAFT) technology, a useful life prediction model for ball bearings has been developed successfully. Finally, a set of accelerated bearing run-to-failure experiments is carried out, with the experimental results showing that the new proposed methods are greatly superior to those, based on L10 bearing life prediction, currently being used.     

基于RBF神经网络的人脸识别

介绍了RBF神经网络在人脸识别中的应用;通过理论分析和实验效果突出了RBF神经网络在机器学习中的优势。实验结果证明:RBF神经网络具有运算速度快、识别率高、算法简单等特点。在训练样本减少的情况下,该学习机的分类性能没有明显退化。因此,RBF神经网络是一种性能优异的学习机。     

基于一种高精度卷积神经网络的框架结构模型故障诊断研究

框架结构健康检测和故障诊断是机械工程和土木工程等学科中十分重要的科学问题,能够准确诊断出框架结构的故障是保证框架结构健康工作的基本前提.为了提高噪声条件下的框架结构故障诊断精度,对现有的 TICNN(convolution neural networks with training interference)模型进行了...     

Prediction of TC11 single-track geometry in laser metal deposition based on back propagation neural network and random forest

Laser metal deposition process usually involves the nonlinear interaction of multiple factors, such as process parameters and ambient temperature. In this study, random forest (RF) and multilayer back propagation neural network (BPNN) algorithms were employed to investigate the coupling relationship between process parameters and single-track geometry in laser metal deposition for TC11 alloy. With laser power, scanning speed, and powder feeding rate as inputs and track width and height as outputs, 30 different groups of experimental results were adopted as training groups. Their geometries were also predicted. The maximum relative errors of track width and height predictions based on BPNN model were 0.007 % and 0.029 %, respectively, which were lower than those based on RF model. Then, the two models were used to predict the geometry under four new sets of process parameters. Experimental results showed that the maximum error of BPNN model is lower than that of RF model. BPNN model also showed potential to improve cladding quality and efficiency.

     

Prevention of defects in castings using back propagation neural networks

Defects in castings often lead to rejection, which would ultimately result in loss of productivity for a foundry. Expert systems developed by some researchers mostly act as postmortem tools, discussing and analyzing a defect after it has occurred. Though some investigators have attempted to predict a few important defects, a tool that could predict all the possible defects just before the castings are made has not yet been developed. Hence in the present work, an attempt has been made to predict major casting defects like cracks, misruns, scabs, blowholes and air-locks using back-propagation neural networks from the data collected from a steel foundry. The neural network was trained with parameters like green compression strength (GCS), green shear strength (GSS), permeability, moisture percent, composition of the charge and melting conditions as inputs and the presence/absence of defects as outputs. After the training was over, the set of inputs of the casting that is going to be made was fed to the network and the network could predict whether the casting would be sound or defective. If defective, the nature of the defect was also specified by the neural network. The neural network could predict cracks, misruns and air-locks accurately in most of the cases. The neural network could also predict other defects successfully. Investigating the causes followed by altering the moulding parameters and appropriate treatment of the molten metal can prevent the defects that were predicted by the backpropagation neural network.     

单次散射近似研究非视线光传输中的误差

基于蒙特卡罗(Monte Carlo)方法建立了非视线光传输多次散射模型,引入大气光传输系统脉冲响应峰值大小,定量计算了单次散射近似研究非视线光传输时的误差大小。结果表明:大气吸收系数k_a对误差影响较小,误差随大气散射系数k_s和非视线传输光程S的增大而增大;当P值(k_s×S)＞3时,由于多次散射作用明显,误差＞80%;当P＜3时,误差随P值减小而减小,P＜0.3时,误差＜10%,此时单次散射近似可用来研究散射大气中非视线光传输问题。