On the exponentially weighted moving variance

MacGregor and Harris (J Quality Technol 25 (1993) 106–118) proposed the exponentially weighted mean squared deviation (EWMS) and the exponentially weighted moving variance (EWMV) charts as ways of monitoring process variability. These two charts are particularly useful for individual observations where no estimate of variability is available from replicates. However, the control charts derived by using the approximate distributions of the EWMS and EWMV statistics are difficult to interpret in terms of the average run length (ARL). Furthermore, both control charting schemes are biased procedures. In this article, we propose two new control charts by applying a normal approximation to the distributions of the logarithms of the weighted sum of chi squared random variables, which are respectively functions of the EWMS and EWMV statistics. These new control charts are easy to interpret in terms of the ARL. On the basis of the simulation studies, we demonstrate that the proposed charts are superior to the EWMS and EWMV charts and they both are nearly unbiased for the commonly used smoothing constants. We also compare the performance of the proposed charts with that of the change point (CP) CUSUM chart of Acosta‐Mejia (1995). The design of the proposed control charts is discussed. An example is also given to illustrate the applicability of the proposed control charts. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

多因素协调设计提高超视距反舰导弹抗干扰能力

依据超视距反舰导弹的特点和对捕捉概率影响因素的分析,提出一种基于捕捉概率的多因素协调设计的方法,用于提高导弹抗干扰能力.并运用均匀试验设计法统计计算捕捉概率,对仿真结果进行回归分析.最后通过非线性规划对导弹抗干扰能力进行优化计算,论证了该方法的可行性.     

Robust adaptive UKF based on SVR for inertial based integrated navigation

Aiming at the problem that the traditional Unscented Kalman Filtering (UKF) algorithm can’t solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers, this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression (SVR). The algorithm combines the advantages of support vector regression with small samples, nonlinear learning ability and online estimation capability of adaptive algorithm based on innovation. Firstly, the SVR model is trained by using the innovation in the sliding window, and the new innovation is monitored. If the deviation between the estimated innovation and the measured innovation exceeds a given threshold, then measured innovation will be replaced by the predicted innovation, and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation algorithm. Simulation experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF, robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers.     

靶场光学测量数据异方差性检验及修正

为了避免靶场光学测量数据异方差性导致的普通最小二乘估计非有效、显著性检验失去意义和模型的预测失效问题,采用了图形分析、Goldfeld-Quandt和Breusch Pagan Godfrey方法检验光学测量数据异方差性,并针对光学测量数据的异方差性提出分段加权最小二乘修正的方法。通过理论分析,对某设备方位角测量数据进行实验验证,取得了残差平方数据、G-Q检验统计数据、BPG检验统计数据和分段加权最小二乘BPG统计数据。结果表明应用图形分析法对光学测量数据进行异方差性检验最直观和简捷,适合存在明显异方差性的检验,G-Q检验法不适用光学测量数据的异方差性检验,BPG检验理论完整且适合光学测量数据的异方差性检验,分段加权最小二乘方法有效合理,消除了异方差性对回归模型的影响。     

An approach for predicting digital material consumption in electronic warfare

Electronic warfare is a modern combat mode, in which predicting digital material consumption is a key for material requirements planning (MRP). In this paper, we introduce an insensitive loss function (ε) and propose a ε-SVR-based prediction approach. First, we quantify values of influencing factors of digital equipments in electronic warfare and a small-sample data on real consumption to form a real combat data set, and preprocess it to construct the sample space. Subsequently, we establish the ε-SVR-based prediction model based on “wartime influencing factors - material consumption” and perform model training. In case study, we give 8 historical battle events with battle damage data and predict 3 representative kinds of digital materials by using the proposed approach. The results illustrate its higher accuracy and more convenience compared with other current approaches. Taking data acquisition controller prediction as an example, our model has better prediction performance (RMSE = 0.575 7, MAPE (%) = 12.037 6 and R² = 0.996 0) compared with BP neural network model (RMSE = 1.272 9, MAPE (%) = 23.577 5 and R² = 0.980 3) and GM (1, 1) model (RMSE = 2.095 0, MAPE (%) = 24.188 0 and R² = 0.946 6). The fact shows that the approach can be used to support decision-making for MRP in electronic warfare.     

灰色灾变与回归分析法的航空装备不安全事件预测

为提高航空装备不安全事件的预测水平,减少事故造成的人员和财产损失,将灰色灾变与回归分析方法有机结合,提出一种航空装备不安全事件的组合预测方法。该方法先从数据中找出灾变点(灾变发生的日期),通过建立这些灾变点的灰色灾变模型预测未来灾变点,再对这些灾变点上的值构建灰色预测模型,计算出未来灾变点的灾变值;而对于非灾变点,可建立合适的回归分析模型进行预测。为验证其可行性,在某飞行训练基地的航空装备不安全事件频数的数据基础上,建立了灰色灾变回归组合预测模型,结果表明,模型对2001年~2004年预测的相对误差平均控制在6.87%以内,所建立的组合模型,能够比较客观地反映航空装备安全的未来实际状况。     

制导炸弹滚转通道混合灵敏度鲁棒控制器设计

制导炸弹在大空域飞行过程中受到外界干扰和未建模摄动都较大,为了适应较大摄动,采用混合灵敏度方法来设计鲁棒控制器.首先推导了包含S/T/R的广义受控对象的状态空间模型,将混合灵敏度问题化为标准H∞问题.然后针对混合灵敏度问题中如何选取权函数的关键性问题,讨论了权函数选取的一般原则,提出了权函数选取的一般方法,并尝试给出权函数的一般形式.最后采用该方法对制导炸弹滚转通道进行了控制器设计,仿真结果表明:H∞控制器有较好的鲁棒性能,且比传统PID控制有更好的时域性能.     

基于灰色非线性回归模型的故障预测

为克服传统灰色模型的局限性,通过对一阶累加生成序列规律性的分析,将灰色模型和非线性回归模型相结合,构造了一种灰色非线性回归模型。实例仿真结果表明,该模型既拓展了传统灰色模型的适用条件,又比传统灰色模型和非线性回归模型具有更高的预测精度,且适用性广。     

一种基于KNN-SVR的基因表达缺失值的估计方法

为了消除不相似基因对基因表达谱中缺失值估计的影响,提出了一种基于KNN SVR的缺失值估计方法.该方法先通过最近邻法选出与目标基因表达最相似的一组完全基因,再用这些基因通过支持向量回归对缺失值进行估计.还提出了用标准化偏差的方差来度量算法的稳定性和估计值的可信度.该方法通过对基因的过滤提高了缺失值估计的有效性.实验结果表明,KNN SVR法具有较高的估计精度和稳定性.     

基于动态窗口的灰色加权填充算法及应用?

为解决复杂系统中单属性缺失数据填充困难问题，提出了基于动态窗口的灰色加权填充算法。该算法通过建立双向灰色预测模型，并采用模型精度评价系数加权填充缺失数据，有效增强了算法的准确性；提出基于灰色模型评价系数反馈的动态伸缩窗口概念，寻找产生最优模型的训练数据序列，使算法具有良好的鲁棒性和适应性。实验结果表明，该方法在RMSE和MA两项指标上均优于传统的双向灰插值、灰插值、多项式插值等方法。