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．     

Knowledge acquisition in incomplete fuzzy information systems via the rough set approach

Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.     

A fuzzy object-oriented data model for managing vague and uncertain information

In this paper, a fuzzy Object-Oriented Data model (FOOD) is defined based on the extension of a Graph-based Object model (D. Lucarella and A. Zanzi “A graph-oriented data model,” in Database and Expert Systems Applications, R. Wagner and H. Toma, Eds., Springer-Verlag, Berlin, 1996, pp. 197–206), in order to manage both crisp and imperfect information. These capabilities are requisites of many current applications dealing with data of different nature and with complex interrelationships. The model is based on a visual paradigm which supports both the representation of the data semantics and the direct browsing of the information. In the extended model both the database scheme and instances are represented as directed labeled graphs in which the fuzzy and uncertain information has its own representation. ©1999 John Wiley & Sons, Inc.     

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.     

Some applications of fuzzy logic in rule–based expert systems

Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision–making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts' opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems.     

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.     

反向三I算法的连续性和误差传播

对全蕴涵反向三I算法是否满足连续性问题进行了首次研究,并进一步讨论了这类算法对误差的传播性能.文中把模糊推理算法看成是一个模糊集合到另一个模糊集合的映射,选用海明距离作为两模糊集的距离,证明了在模糊假言推理和模糊拒取式推理情形,该算法都拥有连续性;其对误差的放大幅度为2.     

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.     

Implementing a fuzzy expert system for ensuring information technology supply chain

In the business environment, information technology (IT) plays an important role for firms' performance. It provides information flow that makes the supply chain more robust and resilient without undermining its efficiency. Smart systems use artificial intelligence methods for solving problems and facilitating decision‐making through rule‐based deduction. Accordingly, these systems can present specialists' skills and simulate their thinking process. The primary goal of expert systems is to implement knowledge acquisition process by converting knowledge to wisdom. This process is vital for critical decision‐making regarding important issues such as determining necessities of a particular contract. Companies use professional liability insurance of the products and services to ensure the purchasers and prevent potential losses. Although this practice is highly prevalent, there is not any particular procedure for measuring necessities of contracts. The main purpose of this paper is to design a fuzzy expert system for measuring the necessities of professional contracts regarding insurance coverage and improve the supply chain management using IT. This system can measure and report these obligations, considering specifications of each project. Taking into perspective variety of professional services/products, we consider software as a type of professional contracts, extract its important indices and give it to the system as the input. After the necessary stages, the system produces a proper response and presents the generated response to the user. The software of this expert system is web based, and there are four operating layers in its architecture. We implemented this program in MS Visual Studio Framework with C#.NET programming language. Moreover, we implemented MS SQL‐Server Database Management.     

语言真值直觉模糊命题逻辑系统的推理规则

提出了一种基于语言真值直觉模糊代数的直觉模糊命题逻辑系统。基于语言真值格蕴涵代数生成语言真值直觉模糊代数,可同时处理具有可比性或不可比性信息。该方法可以同时处理不确定性问题的正面证据和反面证据。研究了语言真值直觉模糊命题逻辑系统LP(S)的性质,得到了其公理及推理规则,也获得了LP(S)中的证明与定理。实例说明,该方法在处理同时具有可比性和不可比性的直觉模糊决策问题中更灵活、更有效。     

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.     

Research and Design of a Fuzzy Neural Expert System

We have developed a fuzzy neural expert system that has the precision and learning ability of a neural network.Knowledge is acquired from domain experts as fuzzy rules and membership functions.Then,they are converted into a neural network which implements fuzzy inference without rule matching.The neural network is applied to problem-solving and learns from the data obtained during operation to enhance the accuracy.The learning ability of the neural network makes it easy to modify the membership functions defined by domain experts.Also,by modifying the weights of neural networks adaptively,the problem of belief propagation in conventional expert systems can be solved easily.Converting the neural network back into fuzzy rules and membership functions helps explain the inner representation and operation of the neural network.     

模糊空间Petri网的规则表示及动态推理

为便于表示模糊空间Petri网的状态变迁规则,根据空间关联影响区域分布现实特点,提出了空间模糊Petri网中的状态关联影响规则、变迁关联影响规则和多阈值激活规则。依据模糊产生式规则的特点,详细描述了10种具体的推理规则和表示组件,并以实例加以说明。在此基础上,结合模糊空间Petri网的特点提出了动态推理过程算法,可以实现各种空间状态规则因子的转化。动态推理的过程不仅可以获取某种“结果”,而且可以挖掘基于空间位置关联的中间状态及引起中间状态变化的事件,可以有效地指导风险过程预测和控制。     

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。     

利用隶属函数宽度的模糊插值推理方法

提出了一种基于隶属函数的宽度的模糊推理方法,该方法应用范围广,使用于所有正规的凸模糊集,能够保证结果的正规性和凸性,而且能够很好地推广到多输入情况。     

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。     

证据理论与模糊距离不确定性信息融合方法

针对决策支持系统中专家不确定性意见难以融合的问题。提出了一种基于证据理论和模糊距离相结合的决策融合方法。运用模糊距离方法来获得专家的权重和属性指标的相对权重,对专家决策中由于主观认识的局限性带来的不确定性问题进行了研究。运用DS证据理论识别框架计算出概率分配函数,对所有方案进行排序选择,得出最终的决策融合意见。通过实验表明,运用此方法对不确定性信息的融合具有很好的可行性和有效性。