基于模糊c均值聚类算法的控制图模式识别

控制图模式识别能够区分制造过程中的一般因素与异常因素,提高制造过程中的产品质量,减少成本,提高效益。利用蒙特卡洛方法产生样本;采用一维离散小波变换处理原始数据;利用模糊c均值聚类算法进行控制图模式识别。识别准确率99.43%,其标准差为0.002 8。这表明基于该方法的控制图模式识别准确率高,稳定性好,较现有的控制图模式识别方法具有简易、高效等特点。     

基于模糊逻辑的异步电动机转速控制

本文设计了基于模糊逻辑控制的速度控制器,以提高异步电动机矢量控制系统对参数变化和负载扰动的鲁棒性,并通过MABLAB/SIMULIINK仿真将其与PI控制的系统速度响应进行比较,仿真结果表明模糊控制能使系统取得较好的控制性能并具有较强的鲁棒性.     

柔性结构的模糊主动振动控制研究

针对柔性结构在不同激振信号下的振动抑制问题，研究了如何应用模糊控制理论实现柔性结构的主动控制，提出了一种基于连续模糊判决函数的模糊ＰＤ主动振动控制方法，对悬臂梁分别在瞬态、正弦及随机激振下的模糊ＰＤ主动振动控制进行了仿真研究。结果表明：模糊ＰＤ控制对上述三种激振信号的振动均有明显的抑制作用。由于模糊ＰＤ主动振动控制对结构的不依赖性，该方法对难以建模的大型柔性结构的主动振动控制具有实际意义。     

基于模糊神经网络的决策支持系统的应用研究

论述了模糊神经网络的概念及特点，在此基础上提出了基于模糊神经网络的决策支持模型，还介绍了其运行计算的过程与方法，并通过农机选型配套的决策支持实例验证模型，说明模型对复杂的决策问题具有简单方便及快速准确做出反应的优点。     

建筑工程施工模糊工期控制方法

本文通过对建筑工程工期控制的研究，引入模糊工期控制新概念，进而利用模糊专家知识对建筑工期实施控制，确定影响因素，定义逻辑变量，确定模型规划，实施工期控制。     

模糊离散变结构控制系统研究

系统研究了模糊离散变结构控制的设计方法，提出了几种具体设计方案．对于变结构控制而言，模糊逻辑的引入可以使滑模(变结构)控制具备推理和学习能力，控制信号得以柔化，能够减轻或避免一般滑模控制的抖振现象；对于模糊控制而言，滑模控制思想的引入可以大大简化控制结构，保证控制系统的鲁棒稳定性，对模糊控制系统的设计和性能分析提供了一种工具．     

基于CMAC的模糊神经网络方法

结合模糊神经网络和小脑模糊连接控制CMAC理论，提出训练时间短，精度高的CMAC模糊神经网络方法，给出了网络结构，算法，并通过一个维修经费预测实例讲述了这种算法。     

基于神经网络的质量控制图模式识别技术的研究

提出了一种用于质量控制图模式识别的新的神经网络模型，它与以往的神经网络模型相比，具有较强的识别能力和较短的训练时间。     

基于控制图的GPS异常监测数据检验方法研究

为了有效地判别GPS异常监测数据,建立了GPS监测序列异常检验的数学模型,提出利用统计过程控制中的控制图对监测序列进行异常检验和预警的新方法;针对GPS监测数据不服从正态分布的问题,提出利用累积分布函数的核密度估计将其转换为Q统计量,并以此为基础构建基于Q统计量的控制图用于GPS异常波动数据的检验;该文文末利用仿真数据对比分析了休哈特控制图与累积和控制图对不同异常偏移值的检验效果,结果表明两种控制图各有利弊、相互补充,休哈特控制图对于3倍以上标准差的异常偏移能够给出有效的预警,但缺乏小偏移检测的能力,累积和控制图能够精确检测出最小达0.5倍标准差的连续小偏移,但是随着偏移值的增大其误警率会有所增加。     

基于T-S型模糊逻辑的半主动摩擦阻尼器对非线性结构的振动控制

采用T-S(Takagi-Sugeno)型模糊逻辑调节半主动摩擦阻尼器正压力的方法对非线性结构的振动控制问题进行了研究。首先,建立了采用连续Bouc-Wen滞回模型的非线性结构控制系统运动方程,运用时程分析法确定了小震下摩擦阻尼器的初始滑动力,然后在建立半主动控制策略的基础上,采用T-S型模糊逻辑调节作用在半主动摩擦阻尼器上的正压力,实现对非线性结构的振动控制。数值分析结果表明,该方法能有效地减小非线性结构在地震激励下的峰值响应。     

Improved Control Charts for Attributes

The classic control charts for attribute data (p-charts, u-charts, etc.,), are based on assumptions about the underlying distribution of their data (binomial or Poisson). Inherent in those assumptions is the further assumption that the “parameter” (mean) of the distribution is constant over time. In real applications, this is not always true (some days it rains and some days it does not). This is especially noticeable when the subgroup sizes are very large. Until now, the solution has been to treat the observations as variables in an individual's chart. Unfortunately, this produces flat control limits even if the subgroup sizes vary. This article presents a new tool, the p'-chart, which solves that problem. In fact, it is a universal technique that is applicable whether the parameter is stable or not.     

基于APL的偏态控制图优化设计

以平均产品长度(APL)为评价控制图性能的标准,研究了偏态控制图的优化设计问题.针对一般控制图无法有效解决偏态总体的不对称性的情况,采用赋权方差法来构造非对称的偏态控制图,并获得其最优设计模型;最后给出了模型的灵敏度分析及算例.     

模糊自适应滤波的主动控制方法研究

由于主动控制中广泛使用的自适应滤波 - x L MS算法只适用于线性控制问题 ,针对一些非线性问题 ,本文提出利用一种非线性自适应滤波方法——基于模糊逻辑系统的自适应滤波方法来解决一类参考信号与外扰呈非线性函数关系的前馈主动控制问题。仿真结果表明 ,该模糊自适应滤波器优于线性滤波器的控制效果     

A Comparison of Normal Approximation Rules for Attribute Control Charts

Control charts, known for more than 80 years, have been important tools for business and industrial manufactures. Among many different types of control charts, the attribute control chart (np‐chart or p‐chart) is one of the most popular methods to monitor the number of observed defects in products, such as semiconductor chips, automobile engines, and loan applications. The attribute control chart requires that the sample size n is sufficiently large and the defect rate p is not too small so that the normal approximation to the binomial works well. Some rules for the required values for n and p are available in the textbooks of quality control and mathematical statistics. However, these rules are considerably different, and hence, it is less clear which rule is most appropriate in practical applications. In this paper, we perform a comparison of five frequently used rules for n and p required for the normal approximation to the binomial. With this result, we also refine the existing rules to develop a new rule that has a reliable performance. Datasets are analyzed for illustration. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive ―X Control Charts with Sampling at Fixed Times

The idea of a variable sampling interval with sampling at fixed times (VSIFT) has been presented by Reynolds. This paper extends this idea to the other two adaptive ―X charts: the variable sampling rate with sampling at fixed times (VSRFT) ―X chart and the variable parameters with sampling at fixed times (VPFT) ―X chart. The VSIFT, VSRFT and VPFT ―X charts are inclusively called the adaptive with sampling at fixed times (AFT) ―X charts in this paper. The control scheme and the design issue are described and discussed for each of the AFT ―X charts. A comparative study shows that the AFT ―X charts have almost the same detection ability as the traditional adaptive ―X charts. However, from the practical viewpoint, the AFT ―X charts are considered to be more convenient to administer than the traditional adaptive ―X charts. Overall, this paper advances the application of ‘sampling at fixed times’ to the adaptive ―X control charts. Copyright © 2004 John Wiley & Sons, Ltd.     

Generalized Linear Model‐based Control Charts for Discrete Semiconductor Process Data

In previous simple cases generalized linear model (GLM)‐based control charts have been shown to be very effective in detecting shifts in multivariate counts when input variables are measurable. This paper studies the effectiveness of GLM‐based control charts on more complicated data sets with multiple inputs and outputs whose relationships are varied. Results show that the GLM‐based charts were most effective in detecting changes in the means of overdispersed counts (when compared with counts with normal dispersion). The GLM‐based charts were more effective than multiple C charts in detecting changes in the means of counts when multiple complicated relationships exist. Copyright © 2004 John Wiley & Sons, Ltd.