Optimization of correlated multi-response quality engineering by the upside-down normal loss function

Most of the published literature on robust design is basically concerned with a single response. However, the reality is that common industrial problems usually involve several quality characteristics, which are often correlated. Traditional approaches to multidimensional quality do not offer much information on how much better or worse a process is when finding optimal settings. Köksoy and Fan [Engineering Optimization 44 (8): 935–945] pointed out that the upside-down normal loss function provides a more reasonable risk assessment to the losses of being off-target in product engineering research. However, they only consider the single-response case. This article generalizes their idea to more than one response under possible correlations and co-movement effects of responses on the process loss. The response surface methodology has been adapted, estimating the expected multivariate upside-down normal loss function of a multidimensional system to find the optimal control factor settings of a given problem. The procedure and its merits are illustrated through an example.     

Inference for Weibull distribution under generalized order statistics

Based on generalized order statistics from Weibull distribution the approach of Bayesian and non-Bayesian estimation are discussed. We present a simple and efficient simulational algorithm for generating a generalized order statistics sample from any continuous distribution. Specializations to Bayesian and non-Bayesian estimators, some lifetime parameters and confidence intervals of progressive II censoring and record values are obtained and compared with the existing results. Two examples are given to illustrate the proposed estimators and the simulation algorithm.     

Robust finite-time boundedness of multi-agent systems subject to parametric uncertainties and disturbances

In this paper, we consider finite-time control problems for linear multi-agent systems subject to exogenous constant disturbances and impulses. Some sufficient conditions are obtained to ensure the finite-time boundedness of the multi-agent systems, which could be then reduced to a feasibility problem involving linear matrix inequalities. Numerical examples are given to illustrate the results.     

健壮性学习算法

使用统计方法可以对汉英机器翻译的词性标注和句法语义分析阶段产生的歧义进行消歧,在估计过程中往往使用最大可能方法,但是并不是在所有的情况下取最大值都是正确的。为了从所有候选结果中取到正确的结果,本文使用健壮性学习算法。使用这个算法,当正确的候选结果评分不是最高时,仍能通过健壮性算法来调整正确结果的评分,使之最大,并且降低不正确候选的评分。而且,由于训练集与测试集存在不同,使训练集中的错误率最小不能保证测试集中的错误率也最小。因此当考虑训练语料库和测试语料库存在统计变化时,应该使用健壮性学习算法。     

一类不确定性广义非线性系统的鲁棒控制

考虑一类上有不确定性广义非线性系统Ｅｚ＝（Ａ＋ΔＡ）ｚ＋Ｂｖ＋ｆ（ｚ）的鲁棒控制问题,这里Ｅ是奇异的ｆ（ｚ）系统的非线性部分,在适当的假设下,得到了基于状态反馈作用的鲁棒控制存在的条件。     

遗传算法在混合H_2/H_∞优化控制设计中的应用

介绍了现已成为关键智能计算之一的一种新的全局优化搜索算法—遗传算法，并探讨了在混合Ｈ２／Ｈ∞最优指标下优化控制设计中的应用。其最大特点是：本质并行、全局优化，它不要求目标函数连续和可微，比梯度法和随机搜索算法更适合于ＰＩＤ混合Ｈ２／Ｈ∞优化问题。     

Robust Estimation of Rotation Angles from Image Sequences Using the Annealing M-Estimator

A robust method is presented for computing rotation angles of image sequences from a set of corresponding points containing outliers. Assuming known rotation axis, a least-squares (LS) solution are derived to compute the rotation angle from a clean data set of point correspondences. Since clean data is not guaranteed, we introduce a robust solution, based on the M-estimator, to deal with outliers. Then we present an enhanced robust algorithm, called the annealing M-estimator (AM-estimator), for reliable robust estimation. The AM-estimator has several attractive advantages over the traditional M-estimator: By definition, the AM-estimator involves neither scale estimator nor free parameters and hence avoids instabilities therein. Algorithmically, it uses a deterministic annealing technique to approximate the global solution regardless of the initialization. Experimental results are presented to compare the performance of the LS, M- and AM-estimators for the angle estimation. Experiments show that in the presence of outliers, the M-estimator outperforms the LS estimator and the AM-estimator outperforms the M-estimator.     

参数不确定广义大系统的分散鲁棒镇定控制

应用线性矩阵不等式(LMI)方法研究了参数不确定广义大系统的分散鲁棒镇定控制问题，系统中不确定项具有数值界，可不满足匹配条件．基干不确定项的表达形式，给出了存在分散鲁棒控制器的LMI条件．仿真例子表明，LMI方法求解简单、便干应用。     

参数不确定时滞系统的鲁棒稳定性

研究一类具有参数不确定时滞系统的鲁棒稳定性问题．假定其线性部分的参数不确定性由区间摄动模武描述，非线性部分的动态不确定性由积分二次约束(IQC)描述，给出了时滞互联系统鲁棒稳定的充分条件，并根据IQC乘子的特性，将无穷维稳定性检验问题转化为一维检验或有限检验问题．