提高激光位移传感器精度的新方法

本文提出了一种新的提高激光位移传感器精度的方法 ,利用分光器对接收光强实时控制同时利用A/D转换器对像点的峰值采样 ,最大限度降低了被测物体表面给测量带来的误差 ,提高了传感器测量精度。     

提高电感传感器测量精度的方法

分析了传统电感传感器信号的精密测量方法的优缺点,对传感器信号的特点进行了研究,在分析影响电感式传感器测量电路的精度因素的基础上,提出利用频率稳定的晶振来作为激励信号的信号源,采用单运放绝对值进行检波,用三极管功率放大电路来驱动负载等措施来改善电感传感器的检测精度,并通过实验论证了该方法的正确性和可行性.     

用神经网络提高SO2传感器的检测速度

制作了新型IDC结构聚苯胺膜SO2传感器,测得不同SO2浓度的电导响应与恢复时间曲线。从这些气敏特性曲线出发,以电导响应曲线的斜率作为网络的输入,对应的SO2浓度作为输出,建立了BP网络预测推理模式,对四组数据的预测结果表明精度较高（误差小于3％）,具有很好的预测能力。这种方法不同于传统的标定方法,后者需要4min才能稳定的响应,神经网络样本检测不需要达到稳定的响应就可以预测SO2的浓度,从而大大缩短了结果的响应时间（缩短了75％）。     

用神经网络提高SO_2传感器的检测速度

制作了新型 IDC结构聚苯胺膜 SO2传感器,测得不同 SO2浓度的电导响应与恢复时间曲 线。从这些气敏特性曲线出发,以电导响应曲线的斜率作为网络的输入,对应的 SO2浓度作为 输出,建立了 BP网络预测推理模式,对四组数据的预测结果表明精度较高 (误差小于 3％）,具 有很好的预测能力。这种方法不同于传统的标定方法,后者需要 4min才能达到稳定的响应,神经 网络样本检测不需要达到稳定的响应就可以预测 SO2的浓度,从而大大缩短了结果的响应时间 （缩短了 75％）。     

称重传感器蠕变误差的神经网络补偿方法

针对电阻应变式称重传感器存在严重的蠕变误差直接影响称重结果准确度的问题,提出了一种基于神经网络的称重传感器蠕变误差自动补偿模型,并给出了模型的训练算法。对量程为50 kg,C3等级的电阻应变式称重传感器进行了实验,实验结果表明,使用神经网络蠕变误差补偿方法补偿后,称重传感器加载标准砝码30 min内的蠕变误差最大变化量为0.0108 kg,小于国家标准GB/T 7551-200《称重传感器》规定的允许值。     

高精度超声波油水界面计的精度分析和神经网络逼近

首先介绍了高精度油水界面汁的原理,通过分析找到了影响其精度的重要因素为长度长度测量的不准确,用神经网络逼近测量结果,取代直接解析计算,得到了系统误差小于1mm的测量精度。     

用神经网络技术实现的光纤位移传感器

描述了一种智能型位移光纤位移传感器，它用差动比值法减小漂移和不同的反射介质的影响，用人工神经网络技术减小非线性误差。实验结果表明，使用这些技术后，各方面指标都远远优于传统方法。     

基于遗传算法和小波神经网络的传感器输出拟合方法研究

针对最小二乘法、分段线性化、神经网络等拟合方法的不足,提出了解决浓度传感器输出特性拟合的遗传神经网络方法,该方法首先使用遗传算法对浓度传感器的输出特性进行“粗拟合”,再用小波神经网络进行“细拟合”,提出遗传进化停滞算子与自适应变异方法,实验验证该方法的有效性.当环境条件发生变化时,只要测量几组数据对,该方法可自动重新训...     

递归神经网络的模糊随机学习算法

提出了一种新的基于模糊逻辑的Alopex学习算法（FLA）。FLA算法利用模糊逻辑推理实时获得适应于学习过程的适当的算法修正值，克服了Alopex算法中修正值固定不变的缺点，使得随机学习过程在速度、精度和稳定性之间获得平衡。将该算法应用于神经网络的训练，可以无需神经网络的梯度信息和结构信息，因此可以用于具有各种结构特性的递归神经网络对动态系统的学习过程。实验结果表明了FLA算法的有效性。     

Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks

E-commerce refers to a system that allows individuals to purchase and sell things online. The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase. They will purchase the item online and have it delivered to their home within a few days. The goal of this research was to develop machine learning algorithms that might predict e-commerce platform sales. A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search (SFS) based on a Guided Whale Optimization Algorithm (WOA) to optimize the parameter weights of the Bidirectional Recurrent Neural Networks (BRNN). Furthermore, a time series dataset is tested in the experiments of e-commerce demand forecasting. Finally, the results were compared to many versions of the state-of-the-art optimization techniques such as the Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). A statistical analysis has proven that the proposed algorithm can work significantly better by statistical analysis test at the P-value less than 0.05 with a one-way analysis of variance (ANOVA) test applied to confirm the performance of the proposed ensemble model. The proposed Algorithm achieved a root mean square error of RMSE (0.0000359), Mean (0.00003593) and Standard Deviation (0.000002162).     

亚微米超精密车床振动的神经网络控制

提出了以空气弹簧和为被动隔振元件,神经网络控制的电磁作动器作为主动隔振元件的隔振系统,分析了以相对位移、等作为反馈变量条件下的系统振动传递率,证明了主、、被动隔振相结合的隔振系统于超精密度床的有效性。     

神经网同步并行计算机

提出了一种人工神经网络同步计算机结构－ＮＮＳＰＣ，并对模型机进行了性能分析，为人工神经网络计算机的制造提供了理论和实践依据。     

轧制力模型自适应在神经元网络预报轧制力中的应用

在中厚板生产过程中,神经元网络已经运用到中厚板轧制力模型预报中,但是与实际的轧制力相比还存在着较大的误差。为了提高神经元网络预报轧制力精度,将轧制力模型的自适应过程引入到神经元网络用于轧制力预报,应用结果表明,采用本文所述的方法,神经元网络的预测精度得到很大改善,预报精度的相对误差可以控制在±3％以内。     

Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks

Most multivariate quality control procedures evaluate the in‐control or out‐of‐control condition based upon an overall statistic, like Hotelling's T². Although T² is optimal for finding a general shift in mean vectors, it is not optimal for shifts that occur for some subset of variables. This introduces a persistent problem in multivariate control charts, namely the interpretation of a signal that often discourages practitioners in applying them. In this paper, we propose an artificial neural network based model to diagnose faults in out‐of‐control conditions and to help identify aberrant variables when Shewhart‐type multivariate control charts based on Hotelling's T² are used. The results of the model implementation on two numerical examples and one case of real world data are encouraging. Copyright © 2005 John Wiley & Sons, Ltd.     

Intelligent Multiclass Skin Cancer Detection Using Convolution Neural Networks

The worldwide mortality rate due to cancer is second only to cardiovascular diseases. The discovery of image processing, latest artificial intelligence techniques, and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality rate. Efficiently applying these latest techniques has increased the survival chances during recent years. The research community is making significant continuous progress in developing automated tools to assist dermatologists in decision making. The datasets used for the experimentation and analysis are ISBI 2016, ISBI 2017, and HAM 10000. In this work pertained models are used to extract the efficient feature. The pertained models applied are ResNet, InceptionV3, and classical feature extraction techniques. Before that, efficient preprocessing is conducted on dermoscopic images by applying various data augmentation techniques. Further, for classification, convolution neural networks were implemented. To classify dermoscopic images on HAM 1000 Dataset, the maximum attained accuracy is 89.30% for the proposed technique. The other parameters for measuring the performance attained 87.34% (Sen), 86.33% (Pre), 88.44% (F1-S), and 11.30% false-negative rate (FNR). The class with the highest TP rate is 97.6% for Melanoma; whereas, the lowest TP rate was for the Dermatofibroma class. For dataset ISBI2016, the accuracy achieved is 97.0% with the proposed classifier, whereas the other parameters for validation are 96.12% (Sen), 97.01% (Pre), 96.3% (F1-S), and further 3.7% (FNR). For the experiment with the ISBI2017 dataset, Sen, Pre, F1-S, and FNR were 93.9%, 94.9%, 93.9%, and 5.2%, respectively.     

基于神经网络的组播路由调度方法

针对在高速包交换计算机网络中端到端时延及时延抖动限制的组播路由问题，提出了基于Ｈｏｐｆｉｅｌｄ神经网络的组播路由优化算法。实验表明，本算法能根据组播应用对时延及时延抖动限制的要求，快速，有效地构造最优组播树，有较强的实时性。     

基于B样条函数的模糊神经网络

通过研究Ｂ样条函数在模糊系统和神经网络系统中的应用，探讨了通过Ｂ样条函数，结合模糊系统和神经网络的各自特点，构造模糊神经网络的方法，并提出了具体的网络模型以及相应的学习方法。