多粒度粗糙计算理论与方法研究
发布时间：2018-10-08 08:00
【摘要】：迅猛发展的信息技术,特别是现代网络、云计算等技术的广泛应用,数据量呈爆炸式增长态势,同时进入系统的信息来源越来越广,相关层次越来越复杂.具备多源多模态等特征的复杂大数据已经成为现代社会中数据资源和知识发现的主体.人们迫切需要去分析处理这些复杂数据,从中找到有价值的信息.然而面对这些复杂数据,传统的数据处理技术遇到了极大挑战.因此,如何有效、快速地的处理这些复杂数据,并提取出隐含其中的、潜在有用的知识,一直是智能信息处理领域的一个研究重点.作为知识获取和问题求解的重要工具,粒计算方法是在问题求解过程中通过将复杂数据进行信息粒化用信息粒代替样本作为计算的基本单元,从多个角度、多个层次出发对现实问题进行描述、推理与求解,可大大提高计算效率并获得问题更加合理、更加满意的求解.本文将人类解决问题的多粒度思想引入到粗糙数据分析中,系统的开展了基于多粒度粗糙计算的方法研究.这极大地丰富了粗糙数据建模理论研究与应用范畴,有望为多源信息系统下的多粒度信息融合提供一个新途径.获得主要研究成果和创新如下：(1)发展了多源符号型数据和多源模糊型数据信息粒度的结构表示与融合模型.为了拓展多粒度粗糙集的建模能力和应用范围,分别建立了多粒度覆盖粗糙集模型和模糊多粒度决策粗糙集模型,深入探讨了模型的性质,并揭示了这些模型之间的本质差异,为多源粗糙数据分析中的模型选择提供了理论基础和可行的依据.(2)从拓扑学理论的角度探讨了多粒度粗糙集模型的相关理论.定义了多粒度拓扑粗糙空间并讨论了该拓扑空间的重要性质,揭示了多粒度拓扑空间的内部结构,通过定义粒度的重要性度量,并根据保持目标概念的内部和闭包不变原则,提出了一个粒度空间的选择算法,从而进一步完善了多粒度粗糙计算理论.(3)从不同的角度提出了多粒度近似空间的不确定性度量.借鉴广义知识距离的思想,构造了多粒度近似空间的融合信息熵,融合粗糙熵和融合知识粒度,给出了多粒度拓扑粗糙空间的拓扑粒度和拓扑熵；提出了多粒度覆盖粗糙集的粗糙度和粗糙熵,并讨论这些度量的有关重要性质.这些结果将有助于理解多粒度粗糙计算理论作为不确定性问题求解理论的本质.(4)结合证据理论,提出了一类基于证据理论的多粒度融合算法.讨论了乐观／悲观多粒度粗糙近似和经典／模糊证据理论的信任函数之间的关系,给出了多粒度粗糙近似空间证据的基本概率指派获取等问题.借鉴K-Modes聚类的思想完成多个粒结构的聚类,提出了一类基于证据理论的多粒度融合算法.在一定程度上解决了多源不确定信息的定量和定性融合问题,也增强了处理多源信息系统不确定问题求解的能力.(5)提出了三类整体决策性能评价指标.通过分析近似精度和近似质量在度量决策性能的不足基础上,利用最大最小合成方式提出了整体确定度,整体协调性,整体支持度.理论分析和实例验证结果表明,提出的决策规则集的评价方法对未来的预测更合理可靠.通过以上系统研究,本文在多粒度粗糙计算理论与方法研究方面取得了系统的研究结果,发展了多粒度覆盖信息粒度的结构表示和定性的融合方法；从多粒度拓扑理论和多粒度近似空间的不确定性这两个侧面完善了多粒度粗糙计算基本理论；建立了多粒度定性融合算子和定量的证据理论的信任函数之间的关系,发展了一类基于证据理论的多粒度融合算法,提出了整体融合决策性能评价方法,这些成果丰富和发展了多粒度粗糙计算理论和方法,为多粒度粒计算方法能够更好的处理多源复杂数据提供了理论指导和技术支持.
[Abstract]:With the rapid development of information technology, especially the wide application of modern network and cloud computing technology, the amount of data is increasing and the information source of the system is getting wider and more complex. Complex large data with multi-source multi-modal characteristics has become the main body of data resources and knowledge discovery in modern society. There is an urgent need to analyse and process these complex data from which valuable information can be found. However, in the face of these complex data, conventional data processing techniques have encountered great challenges. Therefore, how to efficiently and quickly process these complex data and extract hidden and potentially useful knowledge has been a research focus in the field of intelligent information processing. As an important tool for knowledge acquisition and problem solving, the calculation method is to describe, infer and solve the real problem from multiple perspectives and multiple levels by replacing the complex data with information particles instead of the sample as the basic unit of calculation in the problem solving process. the calculation efficiency can be greatly improved and the problem is more reasonable and the solution is more satisfied. In this paper, the multi-granularity idea of human problem solving is introduced into rough data analysis, and a method based on multi-granularity rough calculation is carried out. This greatly enriches the research and application of rough data modeling theory, and is expected to provide a new way for multi-granularity information fusion in multi-source information system. The main research results and innovation are as follows: (1) The structure representation and fusion model of multi-source symbol data and multi-source fuzzy data information granularity are developed. In order to expand the modeling capability and application range of multi-granularity rough set, a rough set model and fuzzy multi-granularity decision rough set model with multiple granularity are established, the nature of the model is discussed in detail, and the essential difference between these models is revealed. It provides theoretical basis and feasible basis for model selection in multi-source rough data analysis. (2) The correlation theory of multi-granularity rough set model is discussed from the point of view of theory. A multi-granularity topological rough space is defined and the important properties of the topological space are discussed, the internal structure of the multi-granularity topological space is revealed, the importance measure of granularity is defined, and according to the principle of keeping the internal and closed packets of the target concept unchanged, A selection algorithm of granularity space is presented to further improve the theory of multi-granularity coarse computation. (3) The uncertainty measure of multi-granularity approximation space is presented from different angles. Based on the idea of the generalized knowledge distance, the fusion information entropy, the fusion rough entropy and the fusion knowledge granularity of the multi-granularity approximation space are constructed, the topological granularity and the topological entropy of the multi-granularity topological rough space are given, and the roughness and the rough entropy of the multi-granularity coverage rough set are proposed. Some important properties of these measures are discussed and discussed. These results will help to understand the nature of multi-particle size rough calculation theory as the solution of uncertainty problem. (4) Based on the theory of evidence, a kind of multi-granularity fusion algorithm based on evidence theory is proposed. The relationship between optimistic/ pessimistic multi-granularity coarse approximation and trust function of classical/ fuzzy evidence theory is discussed, and the basic probability assignment acquisition of multi-granularity coarse approximation space evidence is given. Based on the idea of K-Modes clustering, a class of multi-granularity fusion algorithm based on evidence theory is proposed. The quantitative and qualitative fusion problem of multi-source uncertain information is solved to a certain extent, and the ability to deal with uncertain problem of multi-source information system is also enhanced. (5) Three types of overall decision-making performance evaluation indexes are proposed. Based on the analysis of the deficiency of the approximate precision and the approximate quality in the metric decision-making performance, the whole determination degree, the overall coordination and the overall support degree are put forward by means of the maximum minimum synthesis method. The results of theoretical analysis and case verification show that the proposed method of decision rule set is more reasonable and reliable for future prediction. Through the above system research, this paper has obtained systematic research results in multi-particle size rough calculation theory and method research, and developed the structure representation and qualitative fusion method of multi-granularity coverage information granularity. The basic theory of multi-granularity coarse computation is improved from the two sides of multi-granularity topological theory and the uncertainty of multi-granularity approximation space, and the relation between the multi-granularity qualitative fusion operator and the trust function of the quantitative evidence theory is established. A kind of multi-granularity fusion algorithm based on evidence theory is developed, and a method for evaluating the whole fusion decision-making performance is proposed, which enriches and develops the theory and method of multi-granularity coarse computation. The method can better deal with multi-source complex data and provide theoretical guidance and technical support for multi-granularity calculation method.
【学位授予单位】：山西大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TP18
本文编号：2256021
[Abstract]:With the rapid development of information technology, especially the wide application of modern network and cloud computing technology, the amount of data is increasing and the information source of the system is getting wider and more complex. Complex large data with multi-source multi-modal characteristics has become the main body of data resources and knowledge discovery in modern society. There is an urgent need to analyse and process these complex data from which valuable information can be found. However, in the face of these complex data, conventional data processing techniques have encountered great challenges. Therefore, how to efficiently and quickly process these complex data and extract hidden and potentially useful knowledge has been a research focus in the field of intelligent information processing. As an important tool for knowledge acquisition and problem solving, the calculation method is to describe, infer and solve the real problem from multiple perspectives and multiple levels by replacing the complex data with information particles instead of the sample as the basic unit of calculation in the problem solving process. the calculation efficiency can be greatly improved and the problem is more reasonable and the solution is more satisfied. In this paper, the multi-granularity idea of human problem solving is introduced into rough data analysis, and a method based on multi-granularity rough calculation is carried out. This greatly enriches the research and application of rough data modeling theory, and is expected to provide a new way for multi-granularity information fusion in multi-source information system. The main research results and innovation are as follows: (1) The structure representation and fusion model of multi-source symbol data and multi-source fuzzy data information granularity are developed. In order to expand the modeling capability and application range of multi-granularity rough set, a rough set model and fuzzy multi-granularity decision rough set model with multiple granularity are established, the nature of the model is discussed in detail, and the essential difference between these models is revealed. It provides theoretical basis and feasible basis for model selection in multi-source rough data analysis. (2) The correlation theory of multi-granularity rough set model is discussed from the point of view of theory. A multi-granularity topological rough space is defined and the important properties of the topological space are discussed, the internal structure of the multi-granularity topological space is revealed, the importance measure of granularity is defined, and according to the principle of keeping the internal and closed packets of the target concept unchanged, A selection algorithm of granularity space is presented to further improve the theory of multi-granularity coarse computation. (3) The uncertainty measure of multi-granularity approximation space is presented from different angles. Based on the idea of the generalized knowledge distance, the fusion information entropy, the fusion rough entropy and the fusion knowledge granularity of the multi-granularity approximation space are constructed, the topological granularity and the topological entropy of the multi-granularity topological rough space are given, and the roughness and the rough entropy of the multi-granularity coverage rough set are proposed. Some important properties of these measures are discussed and discussed. These results will help to understand the nature of multi-particle size rough calculation theory as the solution of uncertainty problem. (4) Based on the theory of evidence, a kind of multi-granularity fusion algorithm based on evidence theory is proposed. The relationship between optimistic/ pessimistic multi-granularity coarse approximation and trust function of classical/ fuzzy evidence theory is discussed, and the basic probability assignment acquisition of multi-granularity coarse approximation space evidence is given. Based on the idea of K-Modes clustering, a class of multi-granularity fusion algorithm based on evidence theory is proposed. The quantitative and qualitative fusion problem of multi-source uncertain information is solved to a certain extent, and the ability to deal with uncertain problem of multi-source information system is also enhanced. (5) Three types of overall decision-making performance evaluation indexes are proposed. Based on the analysis of the deficiency of the approximate precision and the approximate quality in the metric decision-making performance, the whole determination degree, the overall coordination and the overall support degree are put forward by means of the maximum minimum synthesis method. The results of theoretical analysis and case verification show that the proposed method of decision rule set is more reasonable and reliable for future prediction. Through the above system research, this paper has obtained systematic research results in multi-particle size rough calculation theory and method research, and developed the structure representation and qualitative fusion method of multi-granularity coverage information granularity. The basic theory of multi-granularity coarse computation is improved from the two sides of multi-granularity topological theory and the uncertainty of multi-granularity approximation space, and the relation between the multi-granularity qualitative fusion operator and the trust function of the quantitative evidence theory is established. A kind of multi-granularity fusion algorithm based on evidence theory is developed, and a method for evaluating the whole fusion decision-making performance is proposed, which enriches and develops the theory and method of multi-granularity coarse computation. The method can better deal with multi-source complex data and provide theoretical guidance and technical support for multi-granularity calculation method.
【学位授予单位】：山西大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TP18
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