稀疏规则库条件下的模糊推理方法

当模糊规则库是稀疏型时,利用Kóczy线性插值推理方法不能保证推理结论的正规性和凸性,为了解决这一问题,石岩曾提出了插值推理方法的推理条件,当满足这些条件时利用Kóczy线性插值推理方法得到的推理结论也满足正规性和凸性;但是这些条件却限制了模糊推理系统的应用,而且如果多次推理中在同一输入点遇到稀疏情况,必须进行相同的计算才能得到正确的推理结果,这样增加了系统的计算量,降低了系统的速度和效率.因此提出了一种新的稀疏模糊推理方法,不仅能够简单的给出正确的推理结果,还能在相应的位置增加规则,提高规则库的紧密程度.     

模糊推理的统计敏感性分析

在设计模糊逻辑系统时，如何实现其对输入噪声的鲁棒性是一个首要的问题，相应地如何很好地分析其对输入噪声的鲁棒性（也称敏感性分析）也就成了一个重要问题．使用统计的方法，对常见的模糊推理方法进行了敏感性分析．首先以均值与方差为基础，提出了2个模糊集的ε-统计相等的概念；随后导出了常见的模糊推理方法的统计敏感性，这包括链接模糊推理与多规则模糊推理．与前人相关工作不同的是，更着重于模糊推理的方差分析，这一方法从数理统计的角度来看能更好地揭示模糊推理本质的敏感性．     

A graphical interpretation of dependency structures in relational data bases

The designer of a relational data base must use dependency structures of data to model semantic situations that arise in data. He must further ensure that these dependencies are not violated during operations on the data base. In this paper we study a subclass of dependencies, namely, root-dependencies and introduce a common graphical picture (S-diagram) for all of them. This effort offers a possible application of graph theory to the study of relational data bases. The S-diagram offers a pictorial insight to all the root-dependencies. We also discuss, briefly, other possible uses of our work such as automatic constraint checking and recovery of data in a damaged data base.     

An improved fuzzy Kalman filter for state estimation of non-linear systems

The extended fuzzy Kalman filter (EFKF) of non-linear systems which can deal with fuzzy uncertainty effectively has been developed recently. But it seems to be inapplicable to the cases where the states change abruptly or there exist model mismatches in non-linear systems. Therefore, based on the EFKF, a new concept of the improved fuzzy Kalman filter (IFKF) is proposed in this article. Due to the introduction of the extension orthogonality principle given as a criterion to design the new algorithm, the IFKF can track the abrupt changes of the states and has definite robustness against the model mismatches. Finally, computer simulations with a MIMO non-linear model are presented, which illustrate that the proposed IFKF has the strong tracking ability and robustness against the model mismatches.     

Fuzzy relational algebra for possibility-distribution-fuzzy-relational model of fuzzy data

In the real world, there exist a lot of fuzzy data which cannot or need not be precisely defined. We distinguish two types of fuzziness: one in an attribute value itself and the other in an association of them. For such fuzzy data, we propose a possibility-distribution-fuzzy-relational model, in which fuzzy data are represented by fuzzy relations whose grades of membership and attribute values are possibility distributions. In this model, the former fuzziness is represented by a possibility distribution and the latter by a grade of membership. Relational algebra for the ordinary relational database as defined by Codd includes the traditional set operations and the special relational operations. These operations are classified into the primitive operations, namely, union, difference, extended Cartesian product, selection and projection, and the additional operations, namely, intersection, join, and division. We define the relational algebra for the possibility-distribution-fuzzy-relational model of fuzzy databases.     

基于模糊理论的造纸专家系统--知识库的设计

基于模糊理论的造纸专家系统(PMES)的研究对于保障设备的稳定运行,提高产品质量具有重要的意义.本文探讨了模糊理论在知识表示方面如何和专家系统进行有效地结合,并对PMES的知识库进行设计.对知识库采用知识分级的方式,使知识的表达更具有层次性.通过各个知识表中的相互约束关系保证知识的一致性.     

Integration of uncertain and imprecise orders in the MRP method

Nowadays, one of the main difficulties of Production Management is to take into account the increasing uncertainty of the customer demand. In an MRP system, this uncertainty is mainly managed at middle term and through successive actualizations of the planning. We suggest in this paper a way to explicitly model the uncertainty and imprecision of the demand allowing to pass through all the MRPII steps (Material Requirement Planning, Load Planning, Scheduling). This method, named Fuzzy-MRP (F-MRP) allows to visualize at each step a much more rich information for the decision makers, taking into account not only the certain data but also a quantification of the various eventualities that may arise. Decisions requiring a long preparation (sub-contracting, order of components, increase of capacity, etc.) can so be considered earlier, on the base of quantified data.     

基于改进的模糊神经学习算法的模糊规则优化

对“改进的模糊神经学习算法”(DevelopedNeuro-FuzzyLearningAlgorithm)简单介绍,并针对这一新算法的缺点,提出了新的聚类方法得到最佳的规则数,利用模糊权值优化规则来改进这个算法,降低算法的时间复杂度,简化神经网络。     

基于规则的通用专家知识库故障诊断方法

针对故障诊断专家系统实用性与通用性的矛盾,在简要分析专家系统工作原理的基础上,提出了一种以用户为中心,基于规则表达的通用性专家知识库故障诊断方法。将规则推理？模糊决策[1]融为一体,形成一阶梯式故障推理机制;对不同的诊断对象,只要设置好必要知识表达模型,就可自行生成一专用基于规则的专家知识库故障诊断方法,并能自动输出诊断结果。     

三种新的基于相似性的加权模糊推理方法

在模糊专家系统中,模糊推理方法的优劣是衡量系统性能好坏的关键指标。基于相似性的加权模糊推理是针对模糊信息发展的一种既简单又灵活的方法,其关键是模糊集合相似度的定义。在文中,提出了三种新的相似度的定义,讨论了与其相应的模糊推理方法;通过实例验证了三种新方法的有效性,并与已有的几种方法进行了比较分析。     

基于模糊积分的客户分类不确定性优化研究

数据挖掘技术为高效的客户分类提供了强大的支持,然而仅依靠这门技术并不能很好地完成这项任务。因为分类方法的局限性,现实数据存在信息的不确定、不完整、先验知识缺乏,研究对象的复杂性等困难导致的分类不确定性。从这个角度出发,将模糊积分融合方法与数据挖掘技术结合来减小客户分类的不确定性,提出了一种模糊密度修正方法,它利用了训练样本先验静态信息和各分类器识别结果包含的动态信息对模糊密度进行自适应动态赋值。仿真结果表明了它的有效性。     

智能故障诊断专家系统开发平台

针对故障诊断专家系统实用性与通用性的矛盾,在简要分析专家系统工作原理的基础上,提出了一种以用户为中心的通用性故障诊断专家系统设计方案。将规则推理、模糊决策、多传感器信息融合算法融为一体,形成一阶梯式故障推理机制;对不同的诊断对象,只要设置好必要知识模块,就可自行生成一专用故障诊断系统,并能自动输出诊断结果。     

Fuzzy interpolative reasoning for sparse fuzzy rule-based systems based on the ranking values of fuzzy sets

Fuzzy interpolative reasoning is an important research topic of sparse fuzzy rule-based systems. In recent years, some methods have been presented for dealing with fuzzy interpolative reasoning. However, the involving fuzzy sets appearing in the antecedents of fuzzy rules of the existing fuzzy interpolative reasoning methods must be normal and non-overlapping. Moreover, the reasoning conclusions of the existing fuzzy interpolative reasoning methods sometimes become abnormal fuzzy sets. In this paper, in order to overcome the drawbacks of the existing fuzzy interpolative reasoning methods, we present a new fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the ranking values of fuzzy sets. The proposed fuzzy interpolative reasoning method can handle the situation of non-normal and overlapping fuzzy sets appearing in the antecedents of fuzzy rules. It can overcome the drawbacks of the existing fuzzy interpolative reasoning methods in sparse fuzzy rule-based systems.     

基于模糊论的数据挖掘研究综述

近年来,将模糊集理论应用到数据挖掘研究中成为数据挖掘领域的一个研究热点。为追踪其研究进展,探讨未来的研究方向,对模糊集理论在数据挖掘中的主要研究方向(聚类分析、关联挖掘、分类)进行了综述,主要阐述数据和模式的表示、模式相似性计算等关键问题。可以看出,充分利用模糊论强大的模糊数据建模功能,并且与其它智能化处理技术相结合,是当前这一领域研究的主流技术。指出了存在的若干问题,并对研究前景进行展望。     

A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing

This paper presents a novel data fusion paradigm based on fuzzy evidential reasoning. A new fuzzy evidence structure model is first introduced to formulate probabilistic evidence and fuzzy evidence in a unified framework. A generalized Dempster’s rule is then utilized to combine fuzzy evidence structures associated with multiple information sources. Finally, an effective decision rule is developed to take into account uncertainty, quantified by Shannon entropy and fuzzy entropy, of probabilistic evidence and fuzzy evidence, to deal with conflict and to achieve robust decisions. To demonstrate the effectiveness of the proposed paradigm, we apply it to classifying synthetic images and segmenting multi-modality human brain MR images. It is concluded that the proposed paradigm outperforms both the traditional Dempster–Shafer evidence theory based approach and the fuzzy reasoning based approach     

Information and probabilistic reasoning

In this paper, the relationship between information and reasoning is investigated and a parallel reasoning method is proposed based on information theory, in particular the principle of minimum cross entropy. Some technical issues, such as multiple uncertain evidence, complicated constraints, small directed cycles and decomposition of underlying networks, are discussed. Some simple examples are also given to compare the method proposed here with other methods.     

基于加权模糊推理的电子取证入侵重构系统

针对目前电子取证入侵重构多用人工分析的方式导致效率低的问题,将AI领域中加权模糊推理方法引入电子取证入侵重构分析中,建立了基于加权模糊推理的电子取证入侵重构专家系统,该系统采用预先定义好的入侵特征,与取证现场获取的入侵痕迹相匹配,并结合相应的权值来推断实际可能的攻击场景。     

模糊控制技术在立式磨系统控制中的研究与应用

针对立式磨生产工况复杂多变,难用准确的数学模型来描述以及常规模糊控制器的控制效果不理想等问题,通过引入专家系统,提出了基于模糊推理的贴近度决策方法,修正控制回路的运行参数,改善模糊控制器的动态及稳态性能,增强控制系统的适应能力.实验结果表明该方法改善了负荷控制性能,提高了产品质量和磨机生产效率.