广义系统的能控、能观性判别条件

该文讨论广义系统能控、能观性问题,给出了广义系统能控和能观性一种新的判别条件.     

广义系统能控性的几个注记

本文首先给出了广义系统能控性与正常系统能控性的关系,并由此讨论了广义系统最优 经济结构综合问题及能控性程度的度量形式.最后给出了工程算例.     

能控（观）广义系统集的拓扑结构*

本文讨论了能控(观)广义系统集的开性和稠密性,并从能控(观)正则系统集的拓扑结构出发,讨论了相应的广义系统集的连通性,得到了较好的结果,从而将有关正则系统的结果推广到了广义系统。     

Hilbert空间中广义分布参数系统的精确能控性

首先,应用泛函分析和算子理论研究了Hilbert空间中广义算子半群的一些性质,并应用广义算子半群理论给出了Hilbert空间中广义分布参数系统的解的存在唯一性及解的构造性表达式;其次,应用泛函分析及广义算子半群理论讨论了Hilbert空间中广义分布参数系统的精确能控性。给出了精确能控的必要及充分条件。说明了广义分布参数系统精确能控性和稳定性之间的关系。所得结果对广义分布参数的研究有重要的理论意义。     

广义控制系统的强能控性与强能观性

本文考虑正则广义控制系统的强能控性与强能观性,在不分解原系统的情况下,用原系统参数给出了一些新的判定条件。     

离散时滞广义系统的一般解与局部能控性

利用函数的Z－变换,获得了离散时滞广义系统的一般解,由此得到该系统的局部可达性与局部能控性的充分必要条件,同时给出了数值例子。     

广义区间动力系统的能控性

讨论了广义区间动力系统正则性、I-能控与C-能控问题.由于上述问题等价于判别某 个区间矩阵为列满秩,首先得到判别区间矩阵为列满秩的充分条件与充分必要条件,进一步得 到了判别广义区间系统为正则的充分必要条件、I-能控的充分条件与C-能控的充分必要条件. 通过数值实例说明所得到的结果相对于已有结果更具有一般性及有效性.由对偶原理,得到相 应的能观性的判据.     

线性时变广义系统的能控性与能观性问题

讨论了线性时变系统和线性时变广义系统的两个基本问题, 得到了两种判定时变系统能控性与能观性的必要条件, 该判定条件只依赖于系统矩阵A(t)和输入矩阵B(t), 不必计算系统的系统状态转移矩阵, 使得判别时变系统能控性与能观性易于实现. 说明了本文结论是线性定常系统相应结论的自然扩展. 对进一步深入研究时变系统和时变广义系统具有实际启发作用.     

离散广义系统模型的一般响应公式与局部能控性

利用函数的Z-变换,获得了离散广义系统模型的一般响应公式,由此得到该模型的局部可达性与局部能控性的充分必要条件.     

广义分散控制系统的DR-能控性

本文研究广义分散控制系统的ＤＲ－能控性问题，给出了一般情况下的广义分散控制系统ＤＲ－能控的充要条件。     

基于复用距离的cache失效率分析

复用距离已经成为程序cache行为的一种重要度量标准，但高复杂度和可能的内存溢出问题使得其难以应用．本文在引入最大cache大小的基础上提出一种受限的复用距离分析方法．该方法有效地避免了一般复用距离分析可能导致的内存溢出问题，同时使得复用距离分析达到线性时间复杂度．文章通过对一系列整数和浮点程序的实验说明基于该复用距离分析的cache失效率分析的可行性和正确性．     

Distance automata having large finite distance or finite ambiguity

A distance automaton is a (nondeterministic finite) automaton which is equipped with a nonnegative cost function on its transitions. The distance of a word recognized by such a machine quantifies the expenses associated with the recognition of this word. The distance of a distance automaton is the maximal distance of a word recognized by this machine or is infinite, depending on whether or not a maximum exists. We present distance automata havingn states and distance 2 ⁿ – 2. As a by-product we obtain regular languages having exponential finite order. Given a finitely ambiguous distance automaton withn states, we show that either its distance is at most 3 ⁿ – 1, or the growth of the distance in this machine is linear in the input length. The infinite distance problem for these distance automata is NP-hard and solvable in polynomial space. The infinite-order problem for regular languages is PSPACE-complete.A preliminary version of this article appeared in theProceedings of the 15th Symposium on Mathematical Foundations of Computer Science, 1990.     

Distance calculation on strings

A monoid of strings (words) over a finite alphabet is considered. The notion of distance on strings is important in the problem of inductive learning related to artificial intelligence, in cryptography, and in some other fields of mathematics. The distance is defined as a minimum length of the transformation path that transforms one string into another. One example is the Levenstein distance, with the transformations being insertions, deletions, and substitutions of letters. A quadratic algorithm for calculating this distance is known to exist. In this paper, a more general case—insertion and deletion of words of arbitrary length—is considered. For this case, the problem of distance calculation turns out to be unsolvable. The basic results of this work are the formulation of the condition of computability of distance and the algorithm for distance calculation, which is polynomial in string length.     

图聚类的算法及其在社会关系网络中的应用

研究图聚类的算法问题。在基于划分的图聚类中,重点比较点与点之间距离的计算方法及其对聚类结果的影响。由于社会关系网络图中点没有坐标值,所以不能使用欧几里得距离和曼哈坦距离。使用k-medoids聚类算法时,分别采用最短距离和随机漫步距离算法,将DBLP数据集构成的社会关系网络图分类成各个子图,通过实验数据验证两种算法的优劣。实验证明最短距离算法获得聚类效果更为理想,达到了较好的分类效果。     

改进的无线传感器网络DV-Hop定位算法

在众多定位技术中,DV-Hop定位算法是应用最广泛的算法之一,它实现简单,对节点硬件要求较低,但该算法使用跳段距离代替实际距离,而平均每跳距离的计算精度受网络的连通度、节点密度等影响。针对DV-Hop定位算法的缺陷,提出修正平均每跳距离和估计距离来提高定位精度的改进算法,并在3种不同的节点分布环境下进行仿真。仿真实验结果表明,改进后的算法定位精度得到了有效的提高。     

区间复模糊软集的距离测度及其应用

基于区间复模糊软集的概念,定义了多种区间复模糊软集的距离测度公式,包含Hausdorff距离、Hamming距离、Euclidean距离、广义Hausdorff距离、广义Euclidean距离、广义加权Hausdorff距离、广义加权Euclidean距离、加权Hausdorff距离、加权Hamming距离、加权Euclidean距离。提出了除交、并、补运算外的区间复模糊软集的加法、乘法、部分隶属度和部分非隶属度运算以及距离测度之间的运算性质。基于区间复模糊软集距离测度构造了一种TOPSIS决策方法,并将这种决策方法应用于经济分析中,验证了所提方法的可行性。     

三维欧氏距离变换的一种新方法

常见的三维距离变换算法大都是对城市街区、棋盘等二维近似欧氏距离变换算法的三维扩展,得到的依然是近似欧氏距离.提出一种新的三维欧氏距离变换算法,可以得到完全欧氏距离,时间复杂度为O(n3logn).将该算法应用于三维医学CT图像内部软组织的显示,取得了较好的效果.     

An additional branch free algebraic B-spline curve fitting method

Algebraic curve fitting based on the algebraic distance is simple, but it has the disadvantage of inclining to a trivial solution. Researchers therefore introduce some constraints into the objective function in order to avoid the trivial solution. However, this often causes additional branches. Fitting based on geometric distance can avoid additional branches, but it does not offer sufficient fitting precision. In this paper we present a novel algebraic B-spline curve fitting method which combines both geometric distance and algebraic distance. The method first generates an initial curve by a distance field fitting that takes geometric distance as the objective function. Then local topology-preserving calibrations based on algebraic distance are performed so that each calibration does not produce any additional branches. In this way, we obtain an additional branch free fitting result whose precision is close to or even better than that produced by purely algebraic distance based methods. The adopted precision criterion is the geometric distance error rather than the algebraic one. In addition, we find a calibration fatigue phenomenon about calibrating strategy and propose a hybrid mode to solve it.     

The distance of probabilistic fuzzy sets for classification

The probabilistic fuzzy set (PFS) is designed for handling the uncertainties with both stochastic and fuzzy nature. In this paper, the concept of the distance between probabilistic fuzzy sets is introduced and its metric definition is conducted, which may be finite or continuous. And some related distances are discussed. The proposed distance considers the random perturbation in progress by introducing the distance of probability distribution, thus it improves the ability to handle random uncertainties, and some inadequacy of the distance of probability distribution is remedied. Finally, a PFS-based distance classifier is proposed to discuss the classification problem, the numerical experiment shows the superiority of this proposed distance in fuzzy and stochastic circumstance.     

基于分段时间弯曲距离的时间序列挖掘

在时间序列库中的数据挖掘是个重要的课题,为了在挖掘的过程中比较序列的相似性,大量的研究都采用了欧氏距离度量或者其变形,但是欧氏距离及其变形对序列在时间轴上的偏移非常敏感．因此,采用了更鲁棒的动态时间弯曲距离,允许序列在时间轴上的弯曲,并且提出了一种新的序列分段方法,在此基础上定义了特征点分段时间弯曲距离．与经典时间弯曲距离相比,大大提高了效率,而且保证了近似的准确性．