Improved results on frequency‐weighted balanced truncation and error bounds

In this paper, we present some new results on frequency‐weighted balanced truncation which is a significant improvement on Lin and Chiu's frequency‐weighted balanced truncation technique. The reduced‐order models, which are guaranteed to be stable in the case of double‐sided weighting, are obtained by direct truncation. Two sets of simple, elegant and easily calculatable a priori error bounds are also derived. Numerical examples and comparison with other well‐known techniques show the effectiveness of the proposed technique. Copyright © 2011 John Wiley & Sons, Ltd.     

The induced generalized aggregation operators for intuitionistic fuzzy sets and their application in group decision making

In this paper, we present the induced generalized intuitionistic fuzzy ordered weighted averaging (I-GIFOWA) operator. It is a new aggregation operator that generalized the IFOWA operator, including all the characteristics of both the generalized IFOWA and the induced IFOWA operators. It provides a very general formulation that includes as special cases a wide range of aggregation operators for intuitionistic fuzzy information, including all the particular cases of the I-IFOWA operator, GIFOWA operator and the induced intuitionistic fuzzy ordered geometric (I-IFOWG) operator. We also present the induced generalized interval-valued intuitionistic fuzzy ordered weighted averaging (I-GIIFOWA) operator to accommodate the environment in which the given arguments are interval-valued intuitionistic fuzzy sets. Further, we develop procedures to apply them to solve group multiple attribute decision making problems with intuitionistic fuzzy or interval-valued intuitionistic fuzzy information. Finally, we present their application to show the effectiveness of the developed methods.     

小波变换和特征加权融合的人脸识别

在人脸识别领域,提取人脸特征和降低维数是人脸识别的关键。传统的基于小波变换的人脸识别算法仅在小波分解的低频分量上提取用于分类的图像特征,造成了高频分量中部分对识别有利信息的丢失。为了更有效地提取人脸图像特征,提出一种基于小波变换和特征加权融合的人脸识别算法。首先通过小波变换对人脸图像进行降维处理,然后对4个小波子图分别运用主成分分析法(PCA)提取特征,并把这4部分特征加权融合,最后利用支持向量机(SVM)进行分类识别。在ORL人脸库上进行实验验证,识别准确率可达到97.5%,实验结果表明该算法能够有效提高人脸识别能力,与传统识别算法相比具有较高的识别准确率和识别速度。     

近程防御系统中多空中目标威胁度分析

研究近程防御系统,快速打击威胁目标的控制是整个系统的主要问题。针对敌方空中目标批次较多、运动状态复杂,而我方防御火力有限,对目标进行有效、精确拦截,需要对目标威胁度排序,依次进行打击。为了正确识别对整个目标过程,进行定性和定量的分析判断,提出了一种行之有效的灰色关联分析的变权威胁度分析方法;进行仿真,生成空中多组动态飞行目标,采用上述算法对空中目标有效识别,并对威胁度进行排序,进行打击。实验结果与专家评估后的结果完全一致,并能够准确而快速的在战场做出决策。     

Power of a Unit‐Root Test and the Initial Condition

Abstract. It is now well known that how the initial observation is generated can have a significant effect on the power of a unit‐root test. In this article, we show that by taking a simple data‐dependent weighted average of the initial condition‐robust test of Elliott and Müller [Journal of Econometrics (2006), forthcoming] and the standard augmented Dickey–Fuller test, we are able to produce a new unit‐root test that can improve power, both asymptotically and in finite samples, over a wide range of possibilities governing the generation of the initial observation.     

加权条件熵在异常检测中的应用

通过分析信息熵在异常检测中的应用和条件熵的固有特性, 提出了一种基于条件熵和加权熵理论的异常检测方法。分析网络流量特征, 选取具有内在相关性的网络特征属性, 给出了加权系数矩阵的公式和异常检测的具体实现方法。通过实验验证了该方法的有效性和实用性, 根据不同属性特征在加权条件熵理论中的应用效果, 分析了不同特征属性选择造成检测效果差异的原因。     

高速以太网中实时相似性匹配算法的研究

在高速以太网中, 针对用户行为的特殊性及数据流五元组的特点, 提出了一种自适应相似性匹配算法(ASMA-HN)。该算法充分考虑了数据流五元组的随机性分布特点, 通过一种改进的XOR-SHIFT算法将数据流分类索引, 并提出了同类数据库(SD)及异类数据库(HD)的概念, 基于距离测度分别设计了加权相似性比较函数, 使得特征值能更精准地匹配到符合要求的数据文件, 提高了整体的匹配效率。通过对校园网实际流量的现场测试, 证明该算法具有较高的精准性及可适用性, 且匹配时间较低, 能很好地满足高速网络中相似性匹配业务的需求。     

半参数广义线性模型惩罚似然估计的几个性质

本文在给出了半参数广义线性模型惩罚似然估计及惩罚准似然估计的基础上,得到了下面几个性质:(Ⅰ)极小惩罚似然估计及惩罚准似然估计等价于惩罚加权最小二乘估计;(Ⅱ)估计的收敛性及加速收敛法;(Ⅲ)估计的一种 Bayes 解释。