基于数据流的视频解码微控制器设计

针对数字视频解码芯片设计中多种视频协议的解析问题,提出一种专用微控制器设计方法。该方法采用面向视频解析的指令集,针对视频解析过程的特点对指令进行特别优化,采用配合该专用微控制器的视频解析模型,较好实现了MPEG1/2、AVS、H.264等视频协议的兼容,保证了解码效率且不会增加芯片面积和功耗。     

优势关系下属性值粗化细化时近似集分析

基于优势关系粗糙集模型反映属性间的偏好情况,实际上多数数据库中的数据是动态变化的。如何利用已有的信息更新近似集对于提高知识发现效率有重要意义。提出不完备信息系统在优势关系下属性值粗化细化的定义,讨论优势关系下不完备信息系统中属性值粗化细化时近似集的变化情况,对比分析优势关系下属性值粗化细化前后的粗糙近似精度和粗糙近似质量。通过实例分析验证了该方法的有效性。     

基于限制非对称相似关系的粗糙集模型

基于不可分辨关系的粗糙集理论不适用于含未知值的不完备信息系统.需要将经典的粗糙集理论不可分辨关系加以扩充才能处理不完备信息系统.目前已经提出了基于容差关系、量化容差关系、限制容差关系、非对称相似关系等的扩充粗糙集理论.但是,这些理论还存在一些局限性.文章提出了一种新的基于限制非对称相似关系的粗糙集扩充模型.理论分析和实验证明,与其它模型相比,可以从基于限制非对称相似关系模型的近似集中获取更多的信息.     

基于置信优势关系粗糙集的近似集动态更新方法

置信优势关系粗糙集是处理不完备有序信息的重要模型, 上、下近似集的计算是核心内容之一. 在实际应用中, 属性集通常会发生变化. 根据属性集的增加或减少, 首先讨论置信优势类及劣势类变化情况, 随之给出上、下近似集增量式的变化规律, 提出相应的近似集动态更新方法. 通过Matlab 在UCI 数据集上的实验结果表明, 与非增量式方法相比, 所提出的置信优势关系粗糙集下的上、下近似集的增量式更新方法可行、高效.

     

Extensions of dominance-based rough set approach in incomplete information system

As one of the useful extensions of classical rough set approach, the dominance-based rough set approach has been successfully applied into multi-criteria decision problems. However, the traditional dominance-based rough set approach is only suitable for the condition attributes, which are positively related with classification analysis. To solve this problem, we propose an extension of the dominance-based rough set approach in incomplete information system by assuming the condition attributes, which are not only positively but also negatively related with classification analysis. Furthermore, by considering the existence of unknown values in incomplete information system, we present the concept of valued dominance relation, which shows the probability of an object is dominating another one with respect to the condition attributes. By using the valued dominance relation, the fuzzy dominance-based rough set models are also studied. A numerical example is employed to substantiate the conceptual arguments. The text was submitted in English by the authors.     

Dominance-based rough set approach and knowledge reductions in incomplete ordered information system

Many methods based on the rough set to deal with incomplete information systems have been proposed in recent years. However, they are only suitable for the incomplete systems with regular attributes whose domains are not preference-ordered. This paper thus attempts to present research focusing on a complex incomplete information system—the incomplete ordered information system. In such incomplete information systems, all attributes are considered as criterions. A criterion indicates an attribute with preference-ordered domain. To conduct classification analysis in the incomplete ordered information system, the concept of similarity dominance relation is first proposed. Two types of knowledge reductions are then formed for preserving two different notions of similarity dominance relations. With introduction of the approximate distribution reduct into the incomplete ordered decision system, the judgment theorems and discernibility matrixes associated with four novel approximate distribution reducts are obtained. A numerical example is employed to substantiate the conceptual arguments.     

广义不完备信息系统中一种拓展粗糙集模型

以同时具有遗漏型和丢失型未知属性值的广义不完备信息系统为研究对象,定义一种用于分类的[α]程度限制优势关系,提出一种基于[α]程度限制优势关系的拓展粗糙集模型,并给出其上、下近似性质。通过一个教师教学质量评估实例,说明这种模型在广义不完备信息系统中处理模糊和不确定知识是有效和可行的。     

Incomplete Information Tables and Rough Classification

The rough set theory, based on the original definition of the indiscernibility relation, is not useful for analysing incomplete information tables where some values of attributes are unknown. In this paper we distinguish two different semantics for incomplete information: the "missing value" semantics and the "absent value" semantics. The already known approaches, e.g. based on the tolerance relations, deal with the missing value case. We introduce two generalisations of the rough sets theory to handle these situations. The first generalisation introduces the use of a non symmetric similarity relation in order to formalise the idea of absent value semantics. The second proposal is based on the use of valued tolerance relations. A logical analysis and the computational experiments show that for the valued tolerance approach it is possible to obtain more informative approximations and decision rules than using the approach based on the simple tolerance relation.     

Dominance relation and rules in an incomplete ordered information system

Rough sets theory has proved to be a useful mathematical tool for classification and prediction. However, as many real‐world problems deal with ordering objects instead of classifying objects, one of the extensions of the classical rough sets approach is the dominance‐based rough sets approach, which is mainly based on substitution of the indiscernibility relation by a dominance relation. In this article, we present a dominance‐based rough sets approach to reasoning in incomplete ordered information systems. The approach shows how to find decision rules directly from an incomplete ordered decision table. We propose a reduction of knowledge that eliminates only that information that is not essential from the point of view of the ordering of objects or decision rules. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 13–27, 2005.     

多准则分类问题中近似集的增量更新方法

在优势关系粗糙集方法(DRSA)的框架下,优势关系可用于处理带有序关系属性(准则)的数据,并且已经被广泛用于处理多准则决策问题。然而在实际应用中,当属性集和对象集发生变化时,信息系统会随之不断更新。在这种动态环境下,DRSA中用于属性约简、规则提取以及决策制定的近似集需要得到相应的更新。针对对象集发生变化时(增加或删除一个对象)的多准则分类问题,采用增量方法来更新近似集并提出两种相应的更新算法DRSA1和DRSA2。同时,对不同情况下的更新原则进行了讨论并给出了相关的理论结果与详细的证明。最后给出算例,并在UCI数据集上进行大量的实验,与非增量的方法(传统的DRSA)进行对比,结果充分体现了所提增量方法的有效性与可扩展性。     

限制优势关系下的加权多粒度粗糙分析方法

作为多粒度粗糙集的推广,提出了限制优势关系下的加权多粒度粗糙分析方法。分析了优势关系粗糙集和多粒度粗糙集的局限性,引入限制优势关系对加权多粒度粗糙集进行改进,充分考虑到属性的偏好关系和粒度的重要性差别,使其适用于不完备的高维或分布式有序信息系统;由此得出粗糙近似,讨论了其与限制优势关系下的乐观、悲观多粒度粗糙集的关系,并对相关性质和定理进行证明;通过实例验证了该方法的有效性和实用性。     

Set-valued ordered information systems

Set-valued ordered information systems can be classified into two categories: disjunctive and conjunctive systems. Through introducing two new dominance relations to set-valued information systems, we first introduce the conjunctive/disjunctive set-valued ordered information systems, and develop an approach to queuing problems for objects in presence of multiple attributes and criteria. Then, we present a dominance-based rough set approach for these two types of set-valued ordered information systems, which is mainly based on substitution of the indiscernibility relation by a dominance relation. Through the lower/upper approximation of a decision, some certain/possible decision rules from a so-called set-valued ordered decision table can be extracted. Finally, we present attribute reduction (also called criteria reduction in ordered information systems) approaches to these two types of ordered information systems and ordered decision tables, which can be used to simplify a set-valued ordered information system and find decision rules directly from a set-valued ordered decision table. These criteria reduction approaches can eliminate those criteria that are not essential from the viewpoint of the ordering of objects or decision rules.     

不完备序信息系统的证据特征及属性约简

不完备有序信息系统粗糙集模型是经典粗糙集的扩展,利用优势关系代替等价关系能很好地处理含未知属性值和带有偏好关系的数据。研究了不完备序信息系统的证据特征,给出了不完备序上、下近似算子,并证明了二者分别与证据理论中的似然函数、信任函数相对应。进而提出了不完备序信息系统的[R]约简、信任约简以及似然约简的概念,探讨了它们之间的一致性,证明了[R]约简与信任约简是等价的,均是保持信任函数和的最小属性集合,得出了似然约简协调集必为[R]约简协调集的结论。     

不完备序值决策系统中的拓展粗集模型及集对分析

在不完备序值决策系统中,根据容差优势关系进行分类过于宽松,而相似优势关系过于严格,根据这样的解释,提出了基于限制容差优势关系的粗糙集模型.进一步地,在不完备序值决策系统中,引入集对分析方法,提出了基于联系度的优势关系粗糙集模型,并进行了实例分析以说明新提出优势关系的有效性.     

基于概率的有序信息系统

若信息系统中所有的条件属性都是偏好有序的,则称此信息系统为有序信息系统。首先,分析了区间值有序信息系统没有蕴含属性值区间上的概率分布信息的缺点,建立了一种基于概率的有序信息系统。然后,在这种信息系统上,研究了关于单调偏好有序属性和非单调偏好有序属性的二元偏好关系,建立了一种基于概率的优势关系,定义了基于这种优势关系的粗糙集模型。最后研究了基于概率的有序决策表及其决策规则。     

有序决策表中的第三种不一致

现实世界中的有序性问题，反映在决策表上相当于在表的属性域上加上优先关系(preferential ordering)或者说是序关系的语义，这种决策表称为有序决策表，有序决策表中的条件属性又称为指标(criterion)，有序决策表中的对象在各个指标上有排序，在决策属性上又有一个总的排序。文献[1，2]指出对于有序决策表中存在一种普通的粗糙集模型不能识别的不一致，并以基于支配关系(dominance relation)的粗糙集模型(dominance-based rough set approach or DRSA)代替基于等价关系的经典粗糙集模型(classic rough set approach or CRSA)，DRSA可以处理这种不一致，文中则进一步指出有序决策表中还存在另一种不一致，不仅在应用上进一步完善了对有序表的处理，而且在理论上丰富了粗糙集中不一致的内涵。     

Learning rules from incomplete training examples by rough sets

Machine learning can extract desired knowledge from existing training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete data sets. If some attribute values are unknown in a data set, it is called incomplete. Learning from incomplete data sets is usually more difficult than learning from complete data sets. In the past, the rough-set theory was widely used in dealing with data classification problems. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete data sets based on rough sets. A new learning algorithm is proposed, which can simultaneously derive rules from incomplete data sets and estimate the missing values in the learning process. Unknown values are first assumed to be any possible values and are gradually refined according to the incomplete lower and upper approximations derived from the given training examples. The examples and the approximations then interact on each other to derive certain and possible rules and to estimate appropriate unknown values. The rules derived can then serve as knowledge concerning the incomplete data set.     

优势-等价关系下序贯三支决策的属性约简

序贯三支决策方法是一种能够表示问题中的多重层次粒度,并将多粒度结合起来解决不确定决策问题的有效途径。优势-等价关系粗糙集则是针对条件属性具有偏好关系的分类问题,提取有序信息,对目标概念进行近似,从而形成决策知识。利用传统的优势关系粗糙集方法进行知识约简和提取的效率低下,而目前大部分序贯三支决策方法则局限在符号值属性的信息系统中,对连续值和有序值不能进行有效处理,造成一定程度的信息丢失。因此,将序贯三支决策的思想应用于优势关系粗糙集模型中,定义了一种新的基于序贯三支决策的属性约简及相应的属性重要度,对具有偏好值属性的信息系统进行更加高效的处理,通过多粒度的表示和关系的研究,加速了知识约简过程。选取了多组UCI数据进行实验,结果表明所提出的基于优势关系的序贯三支决策方法能够在保证约简质量的基础上明显降低时间耗费。     

不完备信息系统中的集对分析方法

考虑了既有遗漏型又有丢失型未知属性值的不完备信息系统。在这种不完备信息系统中，采用集对分析的方法构建了一种新的基于联系度的二元关系，此关系仅满足自反性。在此基础上，分析了这种二元关系在两种特殊情形的不完备信息系统中的表现形式，然后根据新建立的基于联系度的粗集模型，使用决策矩阵讨论了规则生成方法，最后用一个实例说明了此方法的有效性。     

Attribute reduction in ordered information systems based on evidence theory

Attribute reduction is one of the most important problems in rough set theory. However, in real-world lots of information systems are based on dominance relation in stead of the classical equivalence relation because of various factors. The ordering properties of attributes play a crucial role in those systems. To acquire brief decision rules from the systems, attribute reductions are needed. This paper deals with attribute reduction in ordered information systems based on evidence theory. The concepts of plausibility and belief consistent sets as well as plausibility and belief reducts in ordered information systems are introduced. It is proved that a plausibility consistent set must be a consistent set and an attribute set is a belief reduct if and only if it is a classical reduction in ordered information system.