基于上近似的粗糙数据推理研究及应用

发布时间：2018-01-25 06:13

  本文关键词： 粗糙推理空间 粗糙数据推理 上近似 树型推理空间 内涵精度 数据关联　出处：《北京交通大学》2017年博士论文　论文类型：学位论文

【摘要】：信息科学的研究涉及数据处理的各个方面,相关的工作促进了方向的产生,成果的出现推进了学科的发展。作为信息科学的研究课题或研究方向,数据分类、数据约简、数据仓储、数据筛选、数据挖掘、数据推演等针对数据处理的课题既表明了研究领域的宽泛与活跃,也蕴含了理论和应用相结合的研究理念。不同的工作虽各有侧重,但常常涉及共同的研究层面。就数据问题而言,不明确、非确定、似存在或潜存于数据之间的数据联系与这些方向无不相关,同时又在实际当中频繁出现,从而引出了粗糙数据联系的概念。对此的思考和关注促成了粗糙数据推理课题的产生,较少的涉足预示着研究的意义和前沿,加之理论研究将提供算法构建的依据以及程序设计的基础。因此本文聚焦于粗糙数据推理课题的研究,完成的工作集中于如下几个方面:对粗糙集依托的近似空间进行了结构上的扩充,引入了推理关系,产生了粗糙数据推理得以实施的依托环境—粗糙推理空间。为对粗糙数据联系进行描述,在粗糙推理空间中,通过等价关系与推理关系融合信息的上近似,引出了粗糙数据推理的定义,使推理运作于数据之间,产生了课题研究的主题。经对粗糙数据推理的研究,获得了相关的结论,展示了粗糙数据推理的性质,包括:粗糙数据推理保持确定数据联系的特性,粗糙数据推理与上近似中近似信息密切相关的特性,粗糙数据推理具有近似描述功能的特性,粗糙数据推理与路径相互等价的特性,粗糙数据推理对应不同等价关系的特性等。构建了实际问题的粗糙推理空间,描述了汽车制造产业链上企业以不同方式的分类,以及企业之间供货链的确定信息。在该空间中,粗糙数据推理的推演刻画了企业之间潜在供货渠道的粗糙数据联系,提供了智能处理和自动管理的参阅信息,使粗糙数据推理的理论方法在实际中得到了的应用。讨论了特殊的粗糙推理空间—树型推理空间中的粗糙数据推理,展示了以树作为推理关系的特点。在树型推理空间中,利用树包含的层次信息,证明了以树作为推理关系的重要结论:粗糙数据推理的推演依赖于数据位于的层次。由此通过对树型推理空间的细化,展示了细化粗糙数据推理更趋于精确信息的推理特性。同时细化粗糙数据推理的结论可用于汽车制造产业链上供货依赖关系的分析,使理论方法进一步得到了应用。在粗糙推理推理空间中给出了粗糙路径的概念,证明了粗糙路径与粗糙数据推理之间的相互对应联系,从而使粗糙路径用于了粗糙数据推理内涵精度的描述,由此区分了相同形式粗糙数据推理的相异内涵,形成了对粗糙数据联系松散或紧密程度的辨别方法,对于实际应用具有指导性的作用。通过结构化的粒化树构建,并利用粒化树中的层次信息,给出了数据关联的定义,产生了粗糙数据推理的关联推理方法。该方法以关联数据作为桥梁,结合数据的等同、等同的更接近、数据关联的形式、关联情况的数值表示、关联程度的极大性处理等,使两数据类中的数据建立起了关联关系,并以上近似的特定运算作为数据关联判定的充要条件。该方法的特点体现了对粒化树中粒的层次和粒度变化的应用,以及对数据关联和关联程度数值表示的处理。同时讨论与实际问题密切相关,基于粒化树的数据关联方法用于了具体问题的描述,实现了理论联系于实际的研究预期。上述工作以粗糙数据推理作为研究的主体,以数据关联推理作为研究的部分。探究步骤循序渐进,研究细节追求清晰、问题分析逐步推进、整体讨论围绕主题。这些工作包含了课题研究的自身方法,体现了对粗糙数据推理课题与数据关联现象的理解与认识,形成了程序设计的算法基础。同时针对实际问题的模型刻画和实际数据联系的粗糙数据推理描述,展示了理论方法源于实际,实际应用基于理论的研究目的。
[Abstract]:Study on information science involves all aspects of data processing, the related work to promote the direction of production, the results appear to promote the development of the discipline. As the direction of information science research or research data classification, data reduction, data warehousing, data filtering, data mining, data deduction for data processing program show the broad and active research field, but also contains the research concept of combining theory and application. Although different jobs have different emphases, but often involves the research level in common. Data is concerned, is not clear, uncertain, like the presence or potential data between the data associated with these directions are related at the same time, also appeared frequently in practice, which leads to the concept of rough data link. Thinking about this contributed to the rough data reasoning topic, less involved in the study indicates The significance and the frontier, and the theory research will provide the basis algorithm and program design based on rough data reasoning. This thesis focuses on the topic, complete the work focused on the following aspects: to rely on rough set approximation space was expanded on the structure, the reasoning relation, produced rough data reasoning to the implementation of the environment space. Relying on the rough reasoning described for connection to the data in the rough, rough reasoning space, approximate information fusion by equivalence relation and inference relation, leads to a rough number according to the definition of the reasoning, reasoning on data, the research topic. The research of rough data the reasoning, obtained the relevant conclusions, showing the nature of rough data reasoning including rough data reasoning keep determine characteristics of data relationship, rough data and reasoning On the approximate approximation characteristics is closely related to information, rough data reasoning has the characteristics of approximate description of function, characteristics of rough data reasoning and path are equivalent, rough data reasoning corresponding to different equivalence relation properties. Construct the rough reasoning of spatial problems, describes the automobile manufacturing industry chain enterprises to classification in different ways. And between the enterprise supply information to determine the chain. In the space, rough data of deduction depicts contact rough data between enterprise potential supply channels, providing intelligent processing and automatic management of the information, the application of theory and method of rough data reasoning has been discussed in practice. The special space rough reasoning tree type inference in space rough data reasoning, show the tree as inference tree inference relations. In space, the tree contains level The information proved to the tree as an important conclusion: the rough data reasoning of deduction depends on the data in the hierarchy. Thus through the refinement of the tree inference space, showing the characteristics of rough reasoning refinement data reasoning more accurate information. At the same time according to the number of refine the rough reasoning conclusion can be used for the analysis of automobile manufacturing industry chain supply dependency, the theory and method of further application. In the rough reasoning space gives the concept of rough path, proving the corresponding relation between the rough path and rough data reasoning, so that the rough path for the rough data reasoning connotation is described, which distinguishes the different connotation of the same form of rough data the reasoning, formed a discrimination method of rough data or loosely connected closely, is of great significance for practical application through the node. The grain tree construction, and using level of information granulation in the tree, gives the definition of data association, the association reasoning method of rough data reasoning. This method with associated data as a bridge to combine data equivalent, equivalent closer, data association, said the numerical Association. The correlation degree of maximal processing, so that the two data type of data to establish the relationship, necessary and sufficient conditions for a specific operation and above as approximate data association judgment. The characteristic of this method reflects the application of grain in grain and grain tree level changes, and the processing of numerical data association and said the association degree. At the same time discuss closely related problems, data association method for granulation tree based on specific description of the problem, the research realizes the connection of theory to actual expectations. The above work based on rough data reasoning As the research subject to data association reasoning as the research part. On a step by step, study the details of the pursuit of clear, problem analysis step by step, the overall discussion around the theme. The work includes research of its method, reflects the understanding and awareness of the rough data reasoning and data association problem phenomenon, forming algorithm based program design. Describe the rough data model to describe the relation reasoning according to practical problems and actual data, showing the theory stems from the practical application, the purpose of the study is based on the theory.

【学位授予单位】：北京交通大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：TP18
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本文编号：1462218