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集合近似
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  “集合近似”译为未确定词的双语例句
     Set Approximate Neighbourhood and Properties of Knowledge Universe Topology Spaces
     集合近似邻域与知识论域拓扑空间的性质
短句来源
     In this paper,6 point approximate neighborhoods and 9 set approximate neighborhoods are redefined. It is proved that they are complete and exclusive. The basic properties of approximate neighborhoods,the connections among them,other kinds of neighborhoods and approximate operators are studied.
     重新定义了6种点近似邻域和9种集合近似邻域,证明了它们的完备互斥性,并研究了近似邻域的基本性质和与其它邻域、近似算子等的关系,丰富和发展了已有的结果.
短句来源
     In this paper,a concept of a set approximate neighbourhood is defined. Some basic properties of the concept,relationship among various kinds of set approximate neighbourhoods,connections among set approximate neighbourhood and set neighbourhood,approximate operators,point approximate neighbourhood,approximate topology neighbourhood,etc,are studied.
     定义了集合近似邻域,研究了其基本性质,探讨了各种集合近似邻域的关系,以及集合近似邻域与集合邻域、近似算子、点近似邻域、近似拓扑邻域等的关系.
短句来源
     The knowledge universe topology spaces are studied by set approximate neighbourhood,and many important conclusions are achieved.
     用集合近似邻域为手段,研究了知识论域拓扑空间,得出了一些重要结论.
短句来源
  相似匹配句对
     The set
     集合(英文)
短句来源
     Approximation Algorithms for the Problems of Weak Set Cover
     一类弱集合覆盖问题的近似算法
短句来源
     Ring of Set Liu wenqi (Kunming University of science & Technology,Kunming,650093)
     集合
短句来源
     APPROXIMATE DISTRIBUTION OF x~m(n)
     x~m(n)的近似分布
短句来源
     Set Approximate Neighbourhood and Properties of Knowledge Universe Topology Spaces
     集合近似邻域与知识论域拓扑空间的性质
短句来源
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  set approximation
An Optimal Control Algorithm Based on Reachability Set Approximation and Linearization
      
Variational sphere set approximation for solid objects
      
Constant-time distributed dominating set approximation
      
In addition, we introduce a new deterministic set approximation for range spaces with bounded VC-exponent, which we call the δ-relativeε-approximation, and we show how such approximations can be efficiently constructed in parallel.
      
A number of extensions are described, including: a technique for handling weakly dominated outcomes, a Pareto set approximation scheme, and an interactive version that provides access to all Pareto outcomes.
      
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As ctarting inquiry into rough sots,soe formal definitions and proparties of equivalence generalization and special nation of knowledge are firstly intrduced. Knowledge in our apprcach is strictly connected with the variety of classification patterns related to specific parts of real world.A rough set concept as a theor tieal framework for discussions about knowledge is advocated,particularly when imprecise knowledge is of primary concern fundamentats of approximations and rough tets numerical and topological...

As ctarting inquiry into rough sots,soe formal definitions and proparties of equivalence generalization and special nation of knowledge are firstly intrduced. Knowledge in our apprcach is strictly connected with the variety of classification patterns related to specific parts of real world.A rough set concept as a theor tieal framework for discussions about knowledge is advocated,particularly when imprecise knowledge is of primary concern fundamentats of approximations and rough tets numerical and topological characterization of imprecision are introduced.Rough quality and inclusion of sets,as well as approximations of classificatioa are discussed in the paper.

作为研究粗集理论的开始,本文首先给出知识的形式化定义和知识库,知识的等价、推广及特化等概念的一些基本性质,强调知识与分类概念的联系。当涉及到非精确知识时,提倡以粗集概念作为讨论知识的理论基础,因而本文介绍了粗集与集合近似的基本概念,阐述了近似集合的性质及非精确性的数字特征与拓扑特征,最后给出集合的粗等价、粗包含和分类的近似方法。

Compared rough sets with fuzzy theories, rough aproximation of fuzzy sets, rough fuzzy sets are defined based on the concepts of rough sets, and fuzzy rough sets are defined by studied aproximation of sets on similarities of fuzzy partition.Some properties of rough fuzzy sets and fuzzy rough sets are introduced in this paper.Finally the unity of models of knowledge aproximation is discussed.

首先将粗集与模糊集合进行简单对比,并介绍利用粗集的概念考虑模糊集合的粗近似,定义粗模糊集合,利用模糊划分的相似性关系研究集合的近似问题,定义模糊粗集,并介绍粗模糊集合和模糊粗集的基本特性等,最后讨论知识近似模型的统一。

In this paper, a reasoning model in multi agent systems is defined. The rough sets in the models, and the rough inclusion calculus of sets or set connection computing based on rough set approach are established. The equality between sets can't be given in knowledge discovery and data mining; but only the rough inclusion between them is discussed, because it is often not easy to obtain the exact and consistent interpretation between sets in different agent models. In general, an association rule based on decision...

In this paper, a reasoning model in multi agent systems is defined. The rough sets in the models, and the rough inclusion calculus of sets or set connection computing based on rough set approach are established. The equality between sets can't be given in knowledge discovery and data mining; but only the rough inclusion between them is discussed, because it is often not easy to obtain the exact and consistent interpretation between sets in different agent models. In general, an association rule based on decision table often describes, by the support value and confidence coefficients, the degree by which the set of objects satisfying the formula on the left hand side of the association rule is included in the set of objects satisfying the formula on the right hand side of the association rule. Reasoning in distributed environment often requires that a learning interface between multi agents be offered. The interface is an approximate space AS or inference model IM of multi agent defined in this paper. All operations are performed on data table or decision table. Hence, a composite and included operation is defined on the interface(AS or IM). Thus it is possible to obtain the approximation of definable set performed by cooperation between multi agents and the rough included or connection computing between sets in a distributed environment. It is shown with real examples that it is feasible to achieve some goal by actions of multi agents and cooperation between agents in a distributed environment.

定义了多 Agent系统中的推理模型 .建立了在该模型下的 Rough集和基于 Rough集方法的 Rough包含计算或称集合连接计算 .在知识发现和数据挖掘中 ,集合之间往往不是给出它们的相等性 ,而是讨论它们之间的Rough包含或连接 .因为在不同的 Agent中集合之间关系的精确和一致解释往往是不容易获得的 .一般说来 ,一条基于决策表上的规则 ,满足前提公式个体的集合包含于满足结论公式个体的集合常常是用一种支持值和信赖系数来说明 Rough包含或连接的程度 .在分布式环境中多 Agent的情况下计算 Rough包含或连接的程度常常要求提供一个多 Agent之间互学习的界面 ,所以定义了一个多 Agent系统下的推理模型或称近似空间 ,它就是被用作互学习和计算的界面 ,因此在界面上定义了一种合成运算和包含程度计算 .从而可以实现在分布式环境下 ,由多 A-gent互相协作完成可定义集合的近似和集合之间的 Rough包含或连接程度的计算 .最后 ,通过实例说明了在分布式环境下 ,多 Agent活动及其相互配合共同完成一项任务是可行的

 
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