An error variance approach to two-mode hierarchical clustering

A new agglomerative method is proposed for the simultaneous hierarchical clustering of row and column elements of a two-mode data matrix. The procedure yields a nested sequence of partitions of the union of two sets of entities (modes). A two-mode cluster is defined as the union of subsets of the respective modes. At each step of the agglomerative process, the algorithm merges those clusters whose fusion results in the smallest possible increase in an internal heterogeneity measure. This measure takes into account both the variance within the respective cluster and its centroid effect defined as the squared deviation of its mean from the maximum entry in the input matrix. The procedure optionally yields an overlapping cluster solution by assigning further row and/or column elements to clusters existing at a preselected hierarchical level. Applications to real data sets drawn from consumer research concerning brand-switching behavior and from personality research concerning the interaction of behaviors and situations demonstrate the efficacy of the method at revealing the underlying two-mode similarity structure.     

Efficient algorithms for agglomerative hierarchical clustering methods

Whenevern objects are characterized by a matrix of pairwise dissimilarities, they may be clustered by any of a number of sequential, agglomerative, hierarchical, nonoverlapping (SAHN) clustering methods. These SAHN clustering methods are defined by a paradigmatic algorithm that usually requires 0(n ³) time, in the worst case, to cluster the objects. An improved algorithm (Anderberg 1973), while still requiring 0(n ³) worst-case time, can reasonably be expected to exhibit 0(n ²) expected behavior. By contrast, we describe a SAHN clustering algorithm that requires 0(n ² logn) time in the worst case. When SAHN clustering methods exhibit reasonable space distortion properties, further improvements are possible. We adapt a SAHN clustering algorithm, based on the efficient construction of nearest neighbor chains, to obtain a reasonably general SAHN clustering algorithm that requires in the worst case 0(n ²) time and space.Whenevern objects are characterized byk-tuples of real numbers, they may be clustered by any of a family of centroid SAHN clustering methods. These methods are based on a geometric model in which clusters are represented by points ink-dimensional real space and points being agglomerated are replaced by a single (centroid) point. For this model, we have solved a class of special packing problems involving point-symmetric convex objects and have exploited it to design an efficient centroid clustering algorithm. Specifically, we describe a centroid SAHN clustering algorithm that requires 0(n ²) time, in the worst case, for fixedk and for a family of dissimilarity measures including the Manhattan, Euclidean, Chebychev and all other Minkowski metrics.This work was partially supported by the Natural Sciences and Engineering Research Council of Canada and by the Austrian Fonds zur Förderung der wissenschaftlichen Forschung.     

多级连续闪蒸结晶过程的模拟与分析

文中基于物料衡算方程、热量衡算方程、粒数衡算方程、溶解度方程和结晶动力学方程建立了多级连续闪蒸结晶器的数学模型。对于模型中非线性方程组的求解,提出了以结晶内浓度为迭代变量的新迭代算法,该算法初值易确定并具有良好的稳健性和收敛速度,计算结果与文献报道值符合良好。在此基础上以一个KNO₃三级连续闪蒸结晶过程为例考察了首级结晶器的温度和停留时间以及进料浓度对结晶产品粒度分布的影响。     

一种混沌信号噪声的抑制方法

利用局部投影算法中局部协方差矩阵存在特征值突变的现象,提出了改进的噪声抑制方法,解决了原算法中噪声空间和信号空间特征向量难以区分的问题,并运用到Henon映射混沌信号的噪声抑制中。通过仿真研究证实,在低信噪比条件下,改进后的算法与原算法相比,降低了绝对误差,提高了噪声抑制的有效性。     

对分法在多元优化问题中的推广应用

将一维优化方法中的对分法推广应用于无约束多元优化问题的寻优求解中,基于对分法求解的优化问题必须具有有限边界搜索区域的考虑,讨论了问题初始搜索域的确定方法;并进一步给出了对分法推广应用的算法实施过程.最后通过算例与目前现有的几种确定性求导寻优法进行了比较.     

噪声环境下的遗传算法

针对噪声环境下遗传算法解的选取问题提出一种新方法,在此基础之上结合多次采样方法就能够很好地解决遗传算法中的噪声问题。该方法只需增加很少的计算量,就可以获得较为满意的优化解。最后将文中方法与最优列表遗传算法相比较,采用高斯噪声模拟噪声环境,通过函数优化问题来说明该算法的有效性。     

回归函数改良核估计的收敛速度

回归函数的核估计在通常情况下需要核函数具有有界支撑 ,随机变量Y要求具有l阶矩 ,其中l>1。在核函数改进为包括无界支撑甚至不可积 ,并且去掉了对Y的矩的其它要求的情形下 ,讨论了回归函数改良核估计在完全样本及在删失样本情形下的收敛速度 ,得出了与原来情形同样的结论 ,推广和改进了文献 [1- 2 ]的相应结果     

一种基于RSA的加密算法

RSA加密算法是第一个较为完善的公开密钥算法,它的安全性主要依赖于大数分解的难度.在分析了RSA公钥加密系统和背包公钥密码系统存在的安全问题的基础上,将背包公钥密码的思想与RSA算法相结合,形成了一种新的加密算法,它不仅消除了RSA的安全性完全依赖于大数因式分解难度的特性,并且在加密信息的同时,实现了用户身份的验证,有效地防止了中间人攻击.最后,对该算法在智能卡上的应用做了简要介绍.     

基于变分贝叶斯估计方法的双尺度自适应Kalman滤波

针对Kalman滤波在对敌目标估计应用中遇到的量测和过程噪声均未知且时变的情况,提出了一种利用变分贝叶斯估计的双尺度自适应滤波方法。解决了2个关键问题：一是针对量测和过程噪声协方差的共轭后验分布提出了相对转移概率指标,设计了启发式的自适应噪声估计窗口,实现了稳态精度和时变响应性能的综合提升,能适应敌方目标机动性高且统计特性变化快的特点;二是设计了在不同时间尺度上估计过程噪声和量测噪声的协方差方法,解决了在同一时间尺度上使协方差估计值发生严重偏差且增大滤波误差的问题。仿真表明,所提方法能快速跟踪目标状态噪声统计特性的变化并保证估计精度。     

基于多目标进化算法的低轨区域通信星座优化设计

研究利用进化算法实现低轨区域通信星座的多目标优化设计。首先分析、确定低轨星座优化的轨道控制参数，然后将基于Pareto最优概念的多目标进化算法引入星座优化中。结合星座覆盖性能的评价准则，给出了一种利用非劣分层遗传算法(NSGA—Ⅱ)实现星座轨道控制参数优化的框架。最后对具体实例进行了优化仿真，结果表明，该方法可以获得一组分布合理的Pareto解，为星座方案决策提供了有力的支持。