基于BP神经网络的磁滞回线的曲线拟合

BP神经网络通过调节连接权重可以实现以任意精度逼近非线性函数,利用这一特点可对非线性函数关系进行拟合。本利用BP神经网络对测定磁滞回线实验中的数据进行处理,结果表明该方法处理结果精度高,拟合效果好,计算机处理程序通用。     

基于BP神经网络的弗兰克-赫兹实验曲线拟合

BP神经网络通过调整连接权重便可按预定精确度逼近非线性函数,利用这一特点可对非线性函数关系进行拟合.利用BP神经网络对弗兰克-赫兹实验数据进行处理,结果显示该方法处理结果精度高,拟合效果好.     

BP神经网络在大学物理实验中的应用

以验证马吕斯定律实验为例,介绍了BP神经网络在大学物理实验中的应用,并在M atlab环境下通过训练和仿真实现了曲线的拟合.     

用MatLab曲线拟合工具箱处理物理实验数据

以测量玻璃热膨胀系数和折射率温度系数实验、太阳能电池基本特性测量实验为基础,介绍了用MatLab曲线拟合工具箱处理物理实验数据、拟合实验曲线的基本方法,展示了其简单、快捷、高效的特点。     

基于GABP神经网络曲线拟合的快沿电磁脉冲信号源模型求解

为给电子设备的电磁脉冲效应仿真提供准确的快沿电磁脉冲(fast rise-time electromagnetic pulse,FREMP)信号源模型,提出一种基于遗传算法优化BP神经网络(GABP-NN)曲线拟合的信号源模型求解方法;该方法通过示波器对脉冲信号进行采集,利用GABP神经网络对波形曲线进行高精度拟合,提取网络参数建立信号源模型;为进一步获得BP神经网络拟合规律设置对比实验,采用隐含层神经元数为10的GABP神经网络对FREMP信号源进行建模,所得模型拟合度为91.64%;仿真结果表明该方法运算速度快、精度高。     

基于BP神经网络的PID控制器

利用神经网络和反馈控制理论, 提出了一种基于神经网络PID控制器的伺服控制系统结构. 在高精度仿真试验转台的应用中证实, 该方法避免了PID参数的整定难以匹配的问题, 减小了干摩擦对低速运动的影响. 实验表明：方法自适应能力强, 调节品质好, 具有较高的应用价值.     

采用BP神经网络拟合铁磁材料的磁滞回线

介绍了采用BP神经网络模拟人脑智能拟合铁磁材料磁滞回线的方法,给出了实现磁滞回线拟合的BP神经网络结构模型.拟合结果表明,该方法拟合精度较高,对解决非线性拟合问题非常有效.     

基于BP优化算法的PSD非线性校正

针对位置敏感探测器(PSD)固有的非线性,提出一种基于BP优化算法的PSD非线性校正方法。以传统的牛顿算法为基础,推导了Levenberg Marquardt算法,即BP优化算法的相关原理。采用Matlab软件编程,网络采用具有2个中间隐层的结构形式,2个隐层使用的神经元数分别为40和30,最大训练次数取500次,利用sim函数计算并仿真网络输出,网络输出误差均在0.001 mm之内,其中最大误差不超过0.003 mm,实现了对PSD非线性的校正。     

基于粒子群算法优化BP神经网络漏钢预报的研究

针对标准BP神经网络中收敛速度慢以及易陷入局部最优解等问题,利用粒子群算法的全局搜索性,将粒子群算法应用到BP神经网络训练中建立了PSO-BP神经网络模型,结果表明改进模型不仅可以克服传统 BP 网络收敛速度慢和易陷入局部权值的局限问题,而且很大程度地提高了结果精度和 BP 网络学习能力,将此模型应用到结晶器漏钢预报系统中,并用某钢厂采集到的历史数据对该模型进行训练与测试,与标准BP神经网络测试结果进行分析与比较,实验表明PSO-BP网络模型预报更加实时、准确,具有很好的应用前景。     

基于BP神经网络的智能灯光控制系统的研究

智能灯光控制系统是智能家居控制系统的重要组成部分。在分析了目前智能灯光控制系统缺陷与不足的基础上,提出了BP神经网络在智能灯光控制系统的应用,将BP算法嵌入到智能灯光控制系统的数据处理模块,提高控制系统对于数据的处理能力。系统通过引入BP神经网络的自学习能力,改善了智能灯光控制系统智能化程度低的问题。通过实验分析,该系统能够提高智能灯光控制系统的智能性,给人们提供了一个舒适的居家灯光环境。同时,BP神经网络在智能灯光控制系统的应用,对于解决智能家居控制系统解决智能化程度低的问题也有一定的促进作用。     

基于神经网络遗传算法的激光熔钎焊参数优化

针对激光钎焊熔深难以控制的问题,选取激光焊接功率、焊接速度等激光焊接过程中所涉及的控制参数建立基于BP人工神经网络的激光钎焊模型。根据激光钎焊模拟实验的历史数据对焊接熔深进行预测,采用遗传算法对控制参数进行优化,得到目标焊接熔深。运用MATLAB软件建立了针对铝合金/镀锌钢的激光熔钎焊过程的参数的BP人工神经网络模型,并利用遗传算法的并行和群体搜索策略,对其控制参数进行了优化,使得焊接熔深能通过过程参数精确控制,提高了接头性能。     

Pulse coding off-chip learning algorithm for memristive artificial neural network

Memristive neural network has attracted tremendous attention since the memristor array can perform parallel multiply-accumulate calculation (MAC) operations and memory-computation operations as compared with digital CMOS hardware systems. However, owing to the variability of the memristor, the implementation of high-precision neural network in memristive computation units is still difficult. Existing learning algorithms for memristive artificial neural network (ANN) is unable to achieve the performance comparable to high-precision by using CMOS-based system. Here, we propose an algorithm based on off-chip learning for memristive ANN in low precision. Training the ANN in the high-precision in digital CPUs and then quantifying the weight of the network to low precision, the quantified weights are mapped to the memristor arrays based on VTEAM model through using the pulse coding weight-mapping rule. In this work, we execute the inference of trained 5-layers convolution neural network on the memristor arrays and achieve an accuracy close to the inference in the case of high precision (64-bit). Compared with other algorithms-based off-chip learning, the algorithm proposed in the present study can easily implement the mapping process and less influence of the device variability. Our result provides an effective approach to implementing the ANN on the memristive hardware platform.     

布里渊散射谱拟合的混合优化算法

基于布里渊光时域分析仪的全分布式光纤传感系统中,光纤沿途的探测信号含有噪声导致被测量的温度或应变信息难以识别,光谱拟合的精确度对传感信息的识别非常重要。在传感系统低信噪比的情况下,提出了一种提取高精度布里渊散射谱特征的拟合方法,利用小波去噪结合莱文伯-马奈特（LM）算法调节权值后向传输(BP)网络对布里渊散射谱进行特征提取。克服了传统BP神经网络易陷入局部极值的缺点,保证求解的精度。数值仿真表明,该方法适合不同权重比、不同线宽和低信噪比以及大测量范围的情况进行光谱拟合,并且在信噪比为10 dB的情况下得到拟合度均超过0.96。实验结果表明,该方法适用于多种泵浦功率情况下的布里渊散射谱的特征提取,优于传统BP神经网络算法且具有较高的拟合精度。     

基于BP神经网络的日落黄溶液荧光光谱的研究

对食用合成色素日落黄的荧光光谱进行了研究,发现在310~400nm紫外光的激励下,日落黄溶液发出强荧光,峰值荧光强度随浓度的增加先增强后减弱,且荧光谱峰位置出现明显红移。经分析认为,日落黄溶液能产生荧光是因为分子中偶氮键将一个苯环和一个萘环连接在一起,形成大共轭结构,并且取代基-SO3Na与-OH处于萘环的对位,大大增强了日落黄分子的共轭程度,使其具有强的吸光功能,发出强荧光。另外,结合BP神经网络,通过训练好的网络对4种不同浓度的样本进行浓度预测,结果表明相对误差分别为4.269%,6.078%,4.977%和5.308%,相对标准偏差分别为0.448%,0.375%,0.419%和0.414%。实验表明,该方法具有训练速度快、预测结果准确度高等特点,有望成为一种对食用合成色素进行高效、痕量检测的有效方法。     

Prediction of the plasma distribution using artificial neural network

In this work,an artificial neural network (ANN) model is established using a back-propagation training algorithm in order to predict the plasma spatial distribution in an electron cyclotron resonance (ECR)—plasma-enhanced chemical vapor deposition (PECVD) plasma system. In our model, there are three layers:the input layer, the hidden layer and the output layer. The input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is our target output neuron: the plasma density. The accuracy of our prediction is tested with the experimental data obtained by a Langmuir probe, and ANN results show a good agreement with the experimental data. It is concluded that ANN is a useful tool in dealing with some nonlinear problems of the plasma spatial distribution.     

基于BP神经网络的焊点自动光学检测

提出了一种新的表面组装焊点的自动光学检测分类方法。采用单层环形光源获取焊点图像,并根据归一化分割曲线方程将焊点图像分成四部分,分别提取其特征。使用BP神经网络将焊点按照锡量多少分成三类:少锡,容许和多锡。实验结果证明该方法分类正确率达到了99.2%,具有较高的实用价值。     

基于BP神经网络的小样本星图识别方法

为解决星敏感器的星图识别算法高实时性和鲁棒性,采用了BP神经网络;根据飞行轨迹精简了导航星及对应的模式,即一个视场中的一幅星图对应一个惟一的导航模式。采用二维Voronoi图计算向平面上最大空圆,构造了完备的圆视场集合;经过反复比较,选择以恒星为顶点能构造包含视场中所有星的凸多边形的导航模式,以其角距和顶角作为识别向量,具有平移和旋转不变性,并以该模式为BP神经网络的训练样本。仿真试验表明：该方法的识别成功率达100%,识别时间小于20ms。     

An artificial neural network for proton identification in HERMES data

The HERMES time-of-flight (TOF) system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network (ANN) trained to recognize protons from Λ0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron (assigned one proton mass) and a negative hadron (assigned one π- mass) the reconstructed invariant mass lies within the Λ0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero.The trained ANN is capable of identifying protons in independent experimental data, with an efficiencyequivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researchercan trade off between selection efficiency and background rejection power. This simple and convenient methodis applicable to similar detection problems in other experiments.     

Adaptive fuzzy neural network control on the acoustic field in a duct

Base on the principle of the superposition of waves, active noise control is achieved by adaptively tuning a secondary source which produces an anti-noise of equal amplitude and opposite phase with primary source. This paper presents the study on the acoustic attenuation in a duct by using the combination of fuzzy neural network with error back propagation algorithm to control secondary source. The most important advantage of fuzzy inference system is that the structured knowledge is represented in the form of fuzzy IF-THEN rules. But it lacks the ability to accommodate the change of external environments. Combining neural network with fuzzy system can help in this tuning process by adapting fuzzy sets and creating fuzzy rules. The performance of attenuation and control error can be measured by the microphone placed in the downstream of duct. The results of this study, show that the acoustic attenuation by 40 dB for pure-tone noise and nearly 30 dB for dual-tones noise are obtained.