改进遗传算法在非线性热传导参数识别中的应用

建立了基于优化算法的估计材料热传导系数和边界条件的热传导反问题求解方法。该方法以观测的温度值与有限元计算模拟的温度值最小二乘极小化原理为基础,然后采用具有全局搜索能力的遗传算法求解。为了加快收敛速度和提高反演识别精度,采用了浮点编码的遗传算法。根据先验信息,建立了高斯变异策略。数值计算结果表明,所建立的数值反演方法可以用来解决未知的热传导系数和边界条件识别问题,并且具有良好的抗观测噪音能力。     

人工神经网络在材料实验数据处理中的应用

将人工神经网络算法作为一种通用的科学实验数据处理方法引进材料科学。实践表明，在一定的范围内，由神经网络算法对实验数据学习获得的规律可以比较良好地满足工程 对数据处理的要求。     

人工神经网络在数字识别中的应用

BP神经网络是最常用的一种人工神经网络。本文介绍了利用BP神经网络来实现离线手写体数字识别的基本方法,分析了传统BP算法的一些缺点,针对这些缺点指出了一些较新的改进算法。利用MATLAB验证了这些较新的算法。实验数据表明,改进的算法具有较高的识别率。     

BP人工神经网络在二传感器数据融合处理中的应用

针对压力传感器对温度的交叉灵敏度，本文采用BP人工神经网络法对其进行数据融合处理。消除温度对压力传感器的影响，大大提高了传感器的稳定性及其精度。效果良好。     

人工神经网络在优化BaTiO3陶瓷配方研究中的应用

首次将人工神经网络技术用于介电陶瓷的配方性能分析。以BaTiO3为研究对象选取了几种掺杂剂，在均匀实验设计的基础上，用BP人工神经网络对所得实验结果进行了分析，建立了相应配方的数学模型并将其与多重非线性回归模型的结果进行了比较。通过对人工神经网络配方数学模型的二次分析，得到了比多重非线形回归模型更加丰富的配方信息和内在规律，并且用图形化方式直观地表达了出来。在进一步对配方结果的优化和验证的基础上发现实验结果能够较好地符合理论预测，说明人工神经网络对于获得多性能指标要求介电陶瓷的最优化配方具有较好的指导作用。     

基于改进人工神经网络的SMT质量智能鉴别

采用了一种改进的BP神经网络,针对BP神经网络的不足进行了改进:采用变学习率法减少网络训练时间、采用高斯惩罚函数避免局部最小值,并使整个网络能自主调整其隐层节点的数量.运用改进的BP神经网络对于样本进行训练,训练后的神经网络能够较为精确的预测SMT产品质量问题.     

神经网络在梁体结构缺陷识别中的应用

结构中的缺陷识别对于确保结构的安全使用和维护具有重要意义，本文应用神经网络的梁体结构的缺陷参数的识别进行了研究与计算，并用ＢＰ神经网络具有简支梁的缺陷大小，位置与程度进行了预测，其结果与有限元法和能量法所得计算结果有较好的吻合效果。     

结构物理参数时域识别的子结构方法研究

研究了输入、输出信息皆不完备情况下的结构参数识别以及荷载反演问题.阐述了一种通用的子结构动力方程及其参数识别方程建立的基本原理和方法,并针对实际工程检测中子结构参数识别方程的输入特性,分别采用一种与之相适应的分解反演算法或统计平均算法.子结构技术与分解算法或统计平均算法的有效结合,为有限测点条件下的结构参数识别及荷载反演问题提供了一个较好的解决方案.大量的数值计算结果表明,本文提出的方法具有很好的参数识别精度及荷载反演效果.     

Application of the differential neural network observer to the kinetic parameters identification of the anthracene degradation in contaminated model soil

In this work a new technique dealing with differential neural network observer (DNNO), which is related with differential neural networks (DNN) approach, is applied to estimate the anthracene dynamics decomposition and to identify the kinetic parameters in a contaminated model soil treatment by simple ozonation. To obtain the experimental data set, the model soil (sand) is combined with an initial anthracene concentration of 3.24mg/g and treated by ozone (with the ozone initial concentration 16mg/L) during 90min in a reactor by the "fluid bed" principle. The anthracene degradation degree was controlled by UV-vis spectrophotometry and HPLC techniques. Based on the HPLC data, the obtained results confirm that anthracene may be decomposed completely in the solid phase by simple ozonation during 20min and by-products of ozonation are started to be destroyed after 30min of treatment. In the ozonation process the ozone concentration in the gas phase at the reactor outlet is registered by an ozone detector. The variation of this parameter is used to obtain the summary characteristic curve of the anthracene ozonation (ozonogram). Then, using the experimental decomposition dynamics of anthracene and the ozonogram, the proposed DNNO is trained to reconstruct the anthracene decomposition and to estimate the anthracene ozonation constant using the DNN technique and a modified Least Square method.     

Structural parameters identification of uncertain multi-storey shear buildings using fuzzy neural network modelling

Present paper proposes a fuzzy neural network (FNN)-based modelling for the identification of structural parameters of uncertain multi-storey shear buildings. Here, the method is developed to identify uncertain structural mass, stiffness and damping matrices from the dynamic responses of the structure without any optimization processes that are generally used to solve inverse vibration problems. Uncertainty has been taken in term of fuzzy numbers. The governing equations of motion are first solved by the classical method to get responses of the consecutive stories. Further the governing equations of motion are modified based on relative responses of consecutive stories in such a way that the new set of equations can be implemented in a cluster of FNNs. As such the model starts solving the nth floor by FNN modelling to estimate the structural parameters. Subsequently, series of FNN models are used to estimate the parameters for (n ? 1)th storey to the first storey. One may note that single layer FNNs have been used for training for each cluster of the FNN such that the converged weights give the uncertain structural parameters. The initial weights in the FNN architecture are taken as the design parameters in uncertain (fuzzy) form. In order to validate the present model, various example problems of different multi-storey shear structures have been considered. Related results are incorporated in term of tables and graphs. Comparisons between theoretical and identified results are carried out and are found to be in good agreement.     

机器人关节面参数的行波与神经网络混合辨识方法

针对柔性关节机器人,提出了运动状态下关节面参数辨识的新方法。将应用于结构中的行波分析方法与机器人关节旋转变换矩阵相结合,建立机构系统的波导方程。通过各结点力平衡及位移边界条件,得到系统振动激励,从而建立系统振动模型。利用神经网络对振动模型进行求解,将关节刚度和阻尼作为网络权值,辨识出关节动态刚度和阻尼。对3自由度机械臂进行辨识实验研究,实验结果表明该辨识方法是可行的、有效的。     

消能减震建筑结构模态参数识别的贝叶斯方法

为研究消能减震建筑结构中阻尼器的附加阻尼和刚度贡献,建议一种基于贝叶斯统计推断的模态参数识别方法,可用于定量估计阻尼器对结构模态参数的影响,包括阻尼比、频率和振型,以及参数的估计不确定性.为精确建立模态参数与质量和刚度矩阵的函数关系,采用直接模型修正技术进行模型参数化建模,利用模态参数以解析方式重构结构质量、刚度和阻尼...     

Use of a neural network to link AFM etch patterns to plasma parameters

Plasmas play a critical role in depositing thin films or etching fine patterns while manufacturing integrated circuits. A new model for plasma diagnosis is presented. This was accomplished by linking atomic force microscopy (AFM) to plasma parameters using a neural network. Experimental AFM data were collected during the etching of silicon oxynitride films in C₂F₆ inductively coupled plasma. Surface roughness of etched patterns was characterized by means of discrete wavelet transformation. This led to the construction of three vertical (type I), diagonal (type II), and horizontal (type III) wavelet coefficient-based models. The performance of diagnosis models was evaluated in terms of the prediction and recognition accuracies. Both accuracies were optimized as a function of the number of hidden neurons. Comparisons revealed that the type I model yielded the largest recognition and the smallest prediction error. This was demonstrated even under stricter monitoring conditions. More improved diagnosis is expected by enhancing AFM resolution.     

深度卷积神经网络在滑动轴承转子轴心轨迹识别中的应用

针对传统旋转机械智能识别方法需要人为提取特征及诊断精度低的问题,基于深度学习的强大学习能力,提出一种深度卷积神经网络故障诊断模型(Deep Convolutional Neural Network Fault Diagnosis Model,DCNN-FDM)用于轴心轨迹识别.该模型包括输入模块、特征提取模块及分类模块...     

基于卷积神经网络的超声金属材料辨识

传统的金属材料辨识方法会给被检测样品带来一定程度的损伤。文章通过采集金属材料的超声回波时域信号,采用短时傅里叶变换对其进行时频分析,得到包含金属材料微观组织信息的超声时频谱。将目标样品的超声时频谱预处理后作为训练样本,输入到构建好的卷积神经网络中进行训练。然后采集目标样品以及干扰样品的超声频谱图,分别将其输入网络进行辨识。结果表明,神经网络在训练时收敛较快,损失函数在迭代200次后收敛,在经过100次迭代后训练集准确率趋于100%。训练完成的网络模型记录着对应训练样本的特征信息,利用该训练好的网络对待测样本进行辨识,最终可实现超声金属材料辨识。     

Image identification system based on an optical broadcast neural network and a pulse coupled neural network preprocessor stage

We describe the concept of a vision system based on an optoelectronic hardware neural processor. The proposed system is composed of a pulse coupled neural network (PCNN) preprocessor stage that converts an input image into a temporal pulsed pattern. These pulses are inputs to the optical broadcast neural network (OBNN) processor, which classifies the input pattern between a set of reference patterns based on a pattern matching strategy. The PCNN is to provide immunity to the scale, rotation, and translation of objects in the image. The OBNN provides high parallelism and a high speed hardware neural processor.     

A neural network for positron identification by transition radiation detector

A neural network algorithm has been applied in order to distinguish positrons from protons by a transition radiation detector (TRD). New variables are introduced, that simultaneously take into account spatial and energy TRD information. This method is found to be better than the one based on classical analysis: the results improve the detector performance in particle identification for efficiency higher than 90%. The high accuracy achieved with this method is used to identify positrons versus protons with 3 × 10⁻³ contamination, as required by TRAMP-SI cosmic ray space experiment on the NASA Balloon-Borne Magnet Facility.