基于人工神经网络在模具数控加工中的应用

形状特殊的模具在加工之前,应先根据一些已知条件确定出合理的加工参数,而获得最佳加工参数的途径之一就是用最优化技术对问题求解。由于模具数控加工中加工参数与加工质量之间存在高度的非线性关系,而人工神经网络（Artificial Neural Networks简称ANN）又十分擅长表达输入输出关系不明确的高度非线性关系,故基于ANN的优化设计,是解决模具数控加工技术参数优化的有效途径。     

Control of a Robotic Manipulator Using Artificial Neural Networks with On-line Adaptation

An adaptive neural system for positioning control of a PUMA 560 manipulator is presented". The computed torque method was implemented with a Multi-Layer Perceptron with on-line learning. The control scheme is implemented into two phases. The first one is the off-line phase in which the neural network is trained with previously known control actions. The second one is the on-line phase in which the neural network parameters are adapted while controlling the manipulator. The control system is able to respond to changes in the manipulator model and to load disturbances. As will be shown, control system performance is improved with the on-line learning strategy presented in this paper.     

基于神经网络的数模混合电路故障诊断模型设计

神经网络是智能故障诊断中运用最为广泛的诊断方法之一,能应对复杂数字电路和模拟电路故障诊断;基于VC++的神经网络诊断系统具有运算速度快、输入输出界面丰富、易于实现在线故障诊断等特点,而Matlab提供强大的神经网络工具箱,在网络训练方面有优势;利用参数文件将二者优势结合起来,可实现功能完善的数模混合电路智能故障诊断系统;诊断实例表明,使用该方法实现数模混合电路故障诊断系统,具有高建模速度和高诊断精度的特点。     

基于神经网络的炉膛火焰温度的预测

针对短期温度预报在锅炉运行中的重要作用,本文利用神经网络的非线性映射特性,提出一种用BP神经网络进行建模与预测并验证的方法;讨论了预估模型输入向量的选择和数据的预处理方法,对网络中间层不同神经元数目的选择进行训练比较,从而得到最优网络。通过MATLAB仿真,证实了该方法的有效性和可行性。实验证明,BP网络应用在炉膛火焰温度的预测中是可行的。     

Artificial Neural Networks Manipulation Server: Research on the Integration of Databases and Artificial Neural Networks

This paper proposes a new whole and distributed integration approach between Artificial Neural Networks (ANNs) and Databases (DBs) taking into account the different stages of the former’s lifecycle (training, test and running). The integration architecture which has been developed consists of an ANN Manipulation Server (AMS) based on a client-server approach, which improves the ANNs’ manipulation and experimentation capabilities considerably, and also those of their training and test sets, together with their modular reuse among possibly remote applications. Moreover, the chances of integrating ANNs and DBs are analysed, proposing a new level of integration which improves the integration features considerably. This level has not been contemplated yet at full reach in any of the commercial or experimental tools analysed up to the present date. Finally, the application of the integration architecture which has been developed to the specific domain of Environmental Impact Assessments (EIAs) is studied. Thus, the versatility and efficacy of that architecture for developing ANNs is tested. The enormous complexity of the functioning of the patterns which rule the environment’s behaviour, and the great number of variables involved, make it the ideal domain for experimenting on the application of ANNs together with DBs.     

基于CMAC神经网络的热连轧精轧温度预报模型

温度是带钢热连轧过程中几个最重要的工艺参数之一,由于温度将直接影响到热札轧制力,因此精确预报各道次,特别是精轧机组各机架的轧制温度,是保证厚度、板形及宽度数学模型命中率的关键.而精轧温度预报技术是热连轧的核心技术.由于传统的模型技术已经不能进一步提高精轧温度的预报精度,针对带钢热连轧精轧温度传统模型的固有缺陷,根据CM...     

基于神经网络和规则的专家系统的应用研究

本文对专家系统在材料加工领域的应用特点及现状进行了分析与研究,采用基于人工神经网络和规则的知识表示和获取方法,实现了一个混合型的专家系统;并结合实例,讨论了神经网络与专家系统的集成,以及系统的程序框架与功能设计、各模块的主要实现思路;最后,通过实验数据验证,系统的泛化结果误差小于6％。     

基于RBF神经网络的粮库温度预测

由于粮库温度是非线性的时间序列,文章提出了基于RBF神经网络的粮库温度预测模型。该模型优于传统的粮库温度分析方法,又避免了BP算法容易陷入局部极小点和收敛速度慢的缺点。根据实验的仿真结果显示,该模型对于粮库温度的预测效果较好。     

Retrieve Snow Depth of North of Xinjiang Region from ARMS 2 Data based on Artificial Neural Network Technology

Based on the characteristics of the microwave signal responding to the snow depth，we use AMSR2 brightness temperature，geo\|location and terrain factor as the inputs of ANN，and snow depth as the desired output to develop an efficiency snow depth retrieve model.We compared the influence of combinations of TB，geo-ocation and terrain factors on the retrieve of snow depth.It is reviewed in this article that，TB of horizontal polarization，latitude perform better than vertical polarization and longitude respectively.Combination of slope and aspect is superior to other combinations of terrain factors.Besides，there are equivalent influence on snow depth of geo\|location and terrain factors.Finally，we compare the performance of four optimal ANN models under different input combinations.At last，we found that the ANN consists TB，latitude，longitude，slope and aspect as inputs is the best model which might fairly simulating the snow depth of Beijiang.     

基于遗传算法的BP神经网络优化策略研究

人工神经网络（ANN）可用作机器人控制器,完成多机器人协作搬运作业。针对这种方法收敛速度较慢,误差较大的不足,本文提出基于遗传算法优化的方法。该方法利用遗传算法优化人工神经网络,通过改变ANN结构和遗传算法操作参数,找到最优网络,提高网络收敛速度。仿真结果证明,该方法的可行性与有效性。     

一种基于函数型神经网络的传感器静态模型辨识方法

本文提出一种基于函数型神经网络的传感器静态模型辨识方法,该神经网络连接系数直接反映了传感器静成模型中的被辨识参数,网络结构简单,具有良好的收敛性,文章将这一方法实际应用到铂热电阻静态模型辨识,仿真结果表明,本方法是可行的。     

基于神经网络求解游戏关卡问题

随着网络游戏的流行,玩家越来越关注游戏的智能化。本文基于人工神经网络中的BP网络算法建立了一个自动过关模型,此模型根据游戏中的不同环境输入神经元,经过隐含神经的不断学习,输出正确的路径,实验表明此模型能够有效地解决游戏关卡问题,并和遗传算法,情境法进行了对比。     

几种基于神经网络的谐波测量方法比较研究

分析了目前已经提出的几种基于神经网络的电力谐波测量方法。从网络结构、测量原理、学习算法以及实现方法等方面进行了比较分析。结果表明：基于自适应线性神经元的谐波测量方法原理清楚、模型简单、实现方便、测量精度高，是一种很有应用前景的谐波测量方法。比较结果对于应用有指导价值。     

基于STFT和神经网络相结合的时变谐波检测方法研究

电力系统中存在大量的时变谐波，传统的检测方法不能对其进行有效的分析。为此，提出了使用短时傅里叶变换（STFT）和神经网络相结合的算法对这类时变谐波进行检测，由于加窗变换所固有的混叠、泄漏等等，利用STFT只能获得谐波的粗略信息，但是可将获得的信息作为神经网络建立的初始参数；给出了STFT窗函数的确定方法；研究了神经网络参数的选择方法；建立了神经网络对谐波做进一步的检测。仿真试验证明了本算法的有效性。     

Application of Feed-forward Artificial Neural Networks to the Identification of Defective Analog Integrated Circuits

This paper presents a new approach for detecting defects in analog integrated circuits using a feed-forward neural network trained by the resilient error back-propagation method. A feed-forward neural network has been used for detecting faults in a simple analog CMOS circuit by representing the differences observed in power supply current of fault-free and faulty circuits. The identification of defects was performed in time and frequency domains, followed by a comparison of results achieved in both domains. We show that resilient back-propagation neural networks can be a very efficient and versatile approach for identifying defective analog circuits. Moreover, this approach is not limited to the supply current analysis, because it also offers monitoring of other circuit parameters. The type of defects detected by the resilient backpropagation neural networks, as well as other possible applications of this approach, are discussed.     

RBF神经网络单基色温度测量及比较

基用数码相机拍摄高温物体图像,将获取的图像进行R、G、B三基色分离,输入RBF神经网络,分别拟合R-T、G-T、B-T之间的非线性关系.实验结果表明,应用RBF神经网络的单基色进行测温有良好的测量精度,其中单基色G通道精度最高且测量范围最大,为最优测量分量.     

用单片机实现双螺杆挤出机内温度的实时控制

文章介绍了以８０３１单片机作核心控制部件的双螺杆挤出机内温度实时控制系统，并详细介绍了该系统的软硬件实施手段及系统特点。     

An investigation of the suitability of Artificial Neural Networks for the prediction of core and local skin temperatures when trained with a large and gender-balanced database

Neural networks have been proven to successfully predict the results of complex non-linear problems in a variety of research fields, including medical research. Yet there is paucity of models utilising intelligent systems in the field of thermoregulation. They are under-utilized for predicting seemingly random physiological responses and in particular never used to predict local skin temperatures; or core temperature with a large dataset. In fact, most predictive models in this field (non-artificial intelligence based) focused on predicting body temperature and average skin temperature using relatively small gender-unbalanced databases or data from thermal dummies due to a lack of larger datasets.This paper aimed to address these limitations by applying Artificial Intelligence to create predictive models of core body temperature and local skin temperature (specifically at forehead, chest, upper arms, abdomen, knees and calves) while using a large and gender-balanced experimental database collected in office-type situations.A range of Neural Networks were developed for each local temperature, with topologies of 1–2 hidden layers and up to 20 neurons per layer, using Bayesian and the Levemberg-Marquardt back-propagation algorithms, and using various sets of input parameters (2520 NNs for each of the local skin temperatures and 1760 for the core temperature, i.e. a total of 19400 NNs). All topologies and configurations were assessed and the most suited recommended. The recommended Neural Networks trained well, with no sign of over-fitting, and with good performance when predicting unseen data. The recommended Neural Network for each case was compared with previously reported multi-linear models. Core temperature was avoided as a parameter for local skin temperatures as it is impractical for non-contact monitoring systems and does not significantly improve the precision despite it is the most stable parameter. The recommended NNs substantially improve the predictions in comparison to previous approaches. NN for core temperature has an R-value of 0.87 (81% increase), and a precision of ±0.46 °C for an 80% CI which is acceptable for non-clinical applications. NNs for local skin temperatures had R-values of 0.85-0.93 for forehead, chest, abdomen, calves, knees and hands, last two being the strongest (increase of 72% for abdomen, 63% for chest, and 32% for calves and forehead). The precision was best for forehead, chest and calves, with about ±1.2 °C, which is similar to the precision of existent average skin temperature models even though the average value is more stable.     

Artificial Neural Network Application to the Friction Stir Welding of Al 6061 Alloy to Stainless Steel 304

The joining of a 6-mm thickness Al 6061 to Stainless steel 304 has been performed by solid state welding. A selection method of optimum friction welding condition using neural networks is proposed. The data used for analyses are the friction stir welding condition, the input parameters of the model consist of welding speed and tool rotation speed. The outputs of the ANN (Artificial Neural Network)model includes resulting parameters, namely, maximum reached temperature,and heating rate for both aluminum allo...     

单片机温度控制系统的设计及实现

介绍在单片机温度控制系统的软硬件设计中的一些主要技术关键环节，该系统主要以8051单片机为核心，由温度检测电路，模／数转换电路，过零检测电路，报警与指示电路，光电隔离与功率放大电路等构成。