多响应试验设计的优化方法研究

提出了一种改进的马氏距离函数法来解决质量工程中的多响应优化问题。与传统的马氏距离法相比,该方法除考虑了响应之间方差—协方差矩阵结构的影响,还考虑了过程的经济性。通过在规定的试验区域内对此函数进行最小化,能够得到一组合理的可控变量组合,从而达到多响应优化的目的。     

基于STFrFT域无穷范数的CT图像边缘检测

针对医学CT图像边缘模糊、对比度低、噪声污染严重等特点,本文提出了短时分数阶傅里叶变换域的向量无穷范数方法,用于CT图像的边缘检测。短时分数阶傅里叶变换域的虚部能够反映非平稳信号的局部信息。首先,对一维非平稳信号做短时分数阶傅里叶变换,得到短时分数阶傅里叶变换域;其次,计算变换域虚部的向量无穷范数,将二维时频面映射为一维信号,得到非平稳信号的边缘信息。实验表明本文方法具有检测信号边缘的能力,能够有效提取CT图像的边缘,对弱边缘信号也具有较好的检测结果。与经典微分算子和小波变换比较表明,本文方法对CT图像边缘检测表现优良,对噪声污染图像具有较好的鲁棒性。     

多响应问题的稳健性设计优化

基于双响应曲面方法提出了一种解决多响应稳健设计的优化方案。对多响应稳健设计优化问题进行分析,提出了双响应曲面方法与满意度函数法相结合的稳健优化设计方法。该方法通过引入多响应均值满意度和多响应方差满意度,将可控因素和噪声因素的波动产生的方差结合到满意度函数中,并考虑了优化结果的稳健性。采用该方法对文献实例进行分析,得到了较好的优化结果。     

基于满意度函数的多响应稳健优化设计研究综述

在21世纪的竞争环境中,满足顾客多样化需求是企业面临的一大挑战。基于此,本文阐述了在产品设计过程中如何同时优化多个质量特性,并使得最终参数符合稳健设计的理念。本文在回顾了响应曲面法、田口三次设计法、满意度函数法的基础上,综合分析了目前多响应稳健优化设计的研究现状,最终提出将信噪比作为衡量多响应稳健性指标的可行性。     

空间范数在压电柔性结构振动控制中的应用研究

在空间范数定义的基础上,推导出了结构模态空间范数的计算公式。考虑压电柔性结构振动控制中作动器分布对结构建模与控制性能的影响,利用模态空间范数度量各个模态对结构动力响应的贡献,并对结构进行了模态选择与模型降阶。利用所建立的降阶模型,设计了一个对受外部干扰结构进行振动抑制的动态输出反馈]]>;     

对协同优化及其响应面方法的研究

本文论述了复杂系统及多学科优化中的协同优化思想和数学模型,指出了协同优化中的一个突出问题,即学科不一致、实际运用中需要对其进行协调处理的局限性。论述了解决学科不一致问题的二次响应面近似方法思想及算法。通过一个实例,介绍了运用二次响应面近似法的方法,仿真了优化过程,验证了二次响应面方法在系统级优化上迭代次数较少,在学科优化上却相对要多的特征。     

基于主成分分析的函数型产品质量特性的优化方法研究

针对质量特性为轮廓（Profile）的输出响应的优化问题展开研究,提出一种基于主成分分析的双响应曲面法和满意度函数相结合的函数响应优化方法。将Profile的每个观测点看成一个独立响应,将Profile问题转化为多响应问题。求得多个观测点的均值和方差的满意度函数值,通过主成分分析法,将多个观测点的均值和方差的满意度函数值转化为主成分综合得分,并将这两者的加权和作为最终的优化指标。本文所提方法可以有效解决观测点之间存在的相关性的问题,并且优化过程同时考虑到每个观测点响应的均值和方差影响。实例证明,该方法简单易行,优化结果满意。     

两相邻结构地震动响应被动优化控制研究

采用在双体单自由度结构间设立Kelvin型被动控制单元的最优参数表达式,并基于等效双体单自由度体系求得了相邻多层剪切型结构间控制器最优参数。利用列举法并基于二次型性能指标值（LQR）确定了控制器优化布置位置,讨论了布置控制器后对结构地震响应的影响以及对结构动力特性的影响,通过数值分析证实了采用等效双体单自由度体系代替相邻多自由度结构体系的正确性,表明了这种被动控制方法能够非常有效地减小在地震作用下相邻结构的地震响应。     

基于主成分分析的多响应稳健性优化方法研究

将主成分分析法与双响应曲面法相结合,提出了一种同时优化响应均值和标准差的多响应优化方法.通过主成分分析,将多个响应的位置和散布特性值转化为主成分综合得分,并以二者的加权和作为优化指标,克服了传统的多响应优化方法只考虑响应均值的不足,将响应的标准差也纳入优化范畴,实现优化的稳健性.实例表明,该方法充分考虑了响应标准差对优化结果的影响,优化结果得到一定改善.     

基于状态空间响应面方法的结构系统可靠度分析

提出一种在有限元分析中判定结构系统是否失效的准则。实例验证该准则具有较高的可信度。依据该准则经有限元分析可确定样本点所在的状态空间。建立起一种重构结构功能函数的统一方法:状态空间响应面方法,解决了一般响应面方法不能求解输出变量不连续分布的问题。该方法以样本点距离失效空间和可靠空间的距离之差作为样本点的输出变量。数值仿真实例表明该方法有一定的广泛性并具有较高的精度。     

Balancing Location and Dispersion Effects for Multiple Responses

In robust design studies, location and dispersion effects play different roles, and balancing them when there are multiple responses is an important and challenging task. We propose to use desirability function to simultaneously optimize multiple responses after assigning weights to reflect the relative importance of location and dispersion effects. Our analytical strategy is to plot the solutions obtained from different weights versus the weights and/or some aspects of the solutions versus the weights. These plots provide valuable information that can be useful in choosing one or more compromise solutions to balance the multiple location and dispersion effects. The proposed approach is illustrated using two real problems. Copyright © 2012 John Wiley & Sons, Ltd.     

MR-PRIM: Patient Rule Induction Method for Multiresponse Optimization

Most of the works in multiresponse surface methodology have been focusing mainly on the optimization issue, assuming that the data have been collected and suitable models have been built. Though crucial for optimization, a good empirical model is not easy to obtain from the manufacturing process data. This article proposes a new approach to solving the multiresponse problem directly without building a model—an approach called patient rule induction method for multiresponse optimization (MR-PRIM). MR-PRIM is an extension of PRIM to multiresponse problems. Three major characteristic features of MR-PRIM are discussed as the new approach is applied to the case of a steel manufacturing process.     

模糊数据中异常点的一种识别方法

针对输入为确定数、输出为LR型模糊数的模糊数据集,利用最小二乘法建立了该模糊数据集的模糊线性回归分析模型及参数估计,用数据删除的方法研究了数据删除模糊线性回归模型,构造了统计诊断量—模糊Cook距离来识别模糊数据集中的异常点,通过数值模拟和对实例的研究,识别出其异常点,证实了本文构造的统计诊断量的有效性.     

A desirability function method for optimizing mean and variability of multiple responses using a posterior preference articulation approach

A desirability function approach has been widely used in multi‐response optimization due to its simplicity. Most of the existing desirability function‐based methods assume that the variability of the response variables is stable; thus, they focus mainly on the optimization of the mean of multiple responses. However, this stable variability assumption often does not apply in practical situations; thus, the quality of the product or process can be severely degraded due to the high variability of multiple responses. In this regard, we propose a new desirability function method to simultaneously optimize both the mean and variability of multiple responses. In particular, the proposed method uses a posterior preference articulation approach, which has an advantage in investigating tradeoffs between the mean and variability of multiple responses. It is expected that process engineers can use this method to better understand the tradeoffs, thereby obtaining a satisfactory compromise solution.     

采用线性回归方法的发动机活塞参数优化设计

活塞是发动机的重要零件,一般的活塞参数优化方法采用反复的实验、数据分析、参数改进,最终确定其主要参数.这种方法效率低,花费大量的人力与物力.作者提出了基于线性回归实现活塞主要参数优化设计的方法,即建立回归模型分析现有的成熟发动机和活塞参数,找出发动机参数与活塞参数之间的相关函数,利用这些函数并根据发动机的要求进行活塞主要参数的设计.     

An Efficient Sequential Optimization Approach Based on the Multivariate Expected Improvement Criterion

An efficient sequential optimization approach for complex computer models was presented by Jones et al. (1998). After fitting a stochastic process model based on an initial space filling design, this model is sequentially refined by the expected improvement criterion. This criterion balances the need to search in areas in the design space where the prediction is optimal with the need to search where the model uncertainty is high. This approach can easily be extended to physical processes. Since in practice the overall quality of products of production processes is assessed by more than one response, a multivariate version of the expected improvement criterion is proposed based on desirability functions. This criterion is then used to optimize a metal spinning process.     

基于EMD和AR模型的滚动轴承故障诊断方法

提出了基于EMD(Empirical Mode Decomposition)和AR模型的滚动轴承故障诊断方法。该方法用EMD将滚动轴承振动信号分解成若干个平稳的IMF(Intrinsic Mode Function)分量，对每一个IMF分量建立AR模型，以模型的自回归参数和残差的方差作为特征向量建立Mahalanobis距离判别函数，进而判断滚动轴承的工作状态和故障类型。实验结果分析表明，该方法能有效地应用于滚动轴承的故障诊断。     

Interior-Point Algorithm for Linear Optimization Based on a New Kernel Function

In this paper,we design a primal-dual interior-point algorithm for linear optimization.Search directions and proximity function are proposed based on a new kernel function which includes neither growth...