利用ANN法预估芳香族多硝基化合物的密度

运用神经网络模型,采用误差反向传播算法,对一系列芳香族多硝基化合物的密度进行了预测.结果表明,芳香族多硝基化合物的密度与其分子结构存在良好的相关性,选用分子结构描述码作为输入特征参数能取得较高的预估精度,预测结果的相对误差一般在±10%以内.     

机械密封环端面热变形的分析与BP网络预测

分析了影响机械密封环端面变形的主要因素,说明了密封环对介质的传热系数及端面热通量的计算方法.对于高速重载机械密封,因密封环端面热通量大,其热变形显著高于力变形.当密封环模型一定时,按正交设计法给出若干端面热通量值作为输入样本,并将有限元法计算得出的端面内径和外径处的相对轴向变形Δz作为输出样本,训练3层BP人工神经网络,对该密封环端面的轴向热变形进行精确预测.正交设计法和人工神经网络方法相结合是密封环端面热通量与热变形之间高度非线性关系分析的有效方法.     

考虑PCA-RBF的变压器标底预测模型研究

针对变压器制造企业投标报价缺乏科学理论指导,准确性低的问题,以准确预估下一批招标的标底价为目标,提出了结合主成分分析(PCA)与人工神经网络(ANN)的变压器标底预测模型-PCA-RBF标底预测模型。模型采用PCA对高维原始变量进行降维处理,提取主成分(PC)作为径向基函数神经网络(RBF)的输入。PCA算法降低了原始变量的维数,去除了各变量间的相关性,在简化ANN结构的同时,提高了预测模型的精度。仿真结果表明,PCA-RBF变压器标底预测模型具有较高的预测精度。     

基于ANN的箱体类零件CAPP系统的工艺决策

将ANN(Artificial Neural Network,人工神经网络)应用于箱体类零件的CAPP系统中,简化了零件的识别和分类。工艺决策是CAPP系统的关键部分,文中探索了一种有效的工艺决策,使箱体类零件的CAPP系统的工艺决策更容易、方便,提高了工作效率。     

A novel approach based on artificial neural network for calibration of multi-hole pressure probes

Imperfections in the manufacturing process of flow measuring probes affect their measuring behavior. Nevertheless, in order to provide the highest possible accuracy, each individual multi-hole pressure probe has to be calibrated before using them in turbomachinery. This paper presents a novel method based on artificial neural networks (ANN) to predict the flow parameters of multi-hole pressure probes. A two-stage ANN approach using multilayer perceptron (MLP) is proposed in this study. The two-stage prediction approach involves two MLP networks, which represent the calibration data and the prediction error. For a given set of inputs, outputs from both networks are combined to estimate the measured value. The calibration data of a 5-hole probe at RWTH Aachen was used to develop and validate the proposed ANN models and two-stage prediction approach. The results showed that the ANN can predict the flow parameters with high accuracy. Using the two-stage approach, the prediction accuracy was further improved compared to polynomial functions, i.e. a commonly used method in probe calibration. Furthermore, the proposed approach offers high interpolation capabilities while preventing overfitting (i.e. failure to fit new data). Unlike polynomials, it is shown that the ANN based method can provide accurate predictions at intermediate points without large oscillations.     

虚拟产品方案设计RQ评估模型关键技术研究

产品设计方案RQ评价模型是虚拟原型逼真设计模型实现的重要模块之一,建立RQ评估模型的目的就是要寻求一种满足用户需求的最佳设计方案,本文对用户满意度的计算模型进行了研究,建立了基于神经网络(ANN)的设计方案RQ评估模型,对设计方案的RQ进行评估,该模型已应用于仪表新产品开发决策支持系统中。     

柔性机构运动参数动态可靠性分析方法研究

研究目的是提出柔性机构运动参数动态可靠性分析方法,给出了柔性机构运动参数动态可靠性分析的广义模型.将驱动力（矩）、摩擦和阻尼力（矩）等作为随机变量,应用蒙特卡罗方法,取得动态参数样本,再利用人工神经网络方法,根据抽取的样本对网络进行训练,统计网络输出的动态参数分布,得到机构动态可靠度.空间站柔性展开机构动态可靠度计算实例表明该方法减少了计算时间.     

人工神经网络在运动控制中的应用

介绍了人工神经元模型与神经网络基本结构。阐述了多层前向网络的工作原理及误差反转（BP）算法,探讨了用于运动控制的单神经元PID控制器的结构与基于BP网络的模糊自适应PID控制,给出了由传统PID控制器,模糊量化处理,系统辨识神经网络NNM和系统控制网络NNC组成的基于BP网络的模糊自适应PID控制器结构,并讨论了人工神经网络在运动控制领域中应用的发展趋势。     

Toward an automatic wheat purity measuring device: A machine vision-based neural networks-assisted imperialist competitive algorithm approach

Wheat product quality is closely related to wheat seed purity. Purity is an important factor that has a considerable impact on wheat product prices in grain storage silos. The aim of this paper was to introduce a machine vision based approach as a primarily step for fabricating an automatic wheat purity determination and grading device. Experimental data consists of 52 color, morphology, and texture characteristic parameters, extracted from images of samples, including four local wheat grades and eight common weed seeds growing in wheat fields of Iran, were used to build the classification models. A new algorithm that combines Imperialist Competitive Algorithm (ICA) and Artificial Neural Networks (ANNs) has been used for two purposes: to find the best characteristic parameters set and to create robust classification models. Based upon the results obtained from this study, the total classification rate of ICA–ANN approach for wheat grains vs. non-wheat seeds, wheat grain classes, and non-wheat seed classes was 96.25%, 87.50%, and 77.22%, respectively.     

CFD and artificial neural network modeling of two-phase flow pressure drop

A large number of experiments in a 2 cm diameter and 6 m length tube were carried out in order to study the two-phase flow regimes and pressure drops in it. The two-phase flow in the experimental tube was modeled using commercial CFD code, Fluent 6.2. An Artificial Neural Network (ANN) with three inputs including gas and liquid velocities and tube slope was designed and trained to predict average pressure drop across the tube. The comparison between CFD and ANN predictions of pressure drops with experimental measurements shows that the CFD results are more accurate than the ANN evaluations for new conditions.