基于模糊推理模型的水泥粉磨专家控制系统研究

针对水泥粉磨这一类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,通过将模糊控制技术与专家系统的有机融合,提出了基于模糊推理的贴近度决策方法,建立了模糊控制规则模型,解决了模糊推理的规则匹配问题,并给出了控制结论的化优化求解模型。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。     

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.     

基于模糊集贴近度的模糊推理系统摄动研究

目前对模糊推理系统规则摄动度量都是基于蕴涵关系进行研究的,而蕴涵算子选取不当必然会导致规则摄动出现误差。多数模糊推理算法也是基于蕴涵关系,这些算法因涉及模糊关系矩阵运算而使算法的计算过程比较复杂,有时推理结果和实际相差甚远。采用一种度量模糊推理系统规则摄动的新方法分析模糊推理系统的摄动,该方法不再使用蕴涵算子,而是基于规则摄动前后模糊集合的贴近度来度量,与以往的方法有着本质的不同,推理结果更贴合实际情况。     

基于Vague集模糊推理的多评价指标模糊决策方法

目前在智能领域中对Vague集的研究已越来越广泛与深入，并运用于决策问题中，有学者已把Vague集用于多评价指标的模糊决策中，但其决策方法在某些时候却难以得到目标。为此，本文提出了一个基于Vague集模糊推理的多评价指标模糊决策方法。在这个方法中，从基于Vague集的模糊推理的观点来看待模糊决策问题。将评价指标和候选方案之间的关系用一组基于Vague集的推理规则来表示，将决策者的要求用一组Vague集来表示，经过模糊推理等过程最后得到决策结果。然后还给出了一个实例说明这种多评价指标模糊决策方法。这个基于Vague集模糊推理的多评价指标模糊决策方法的提出为决策系统提供了一个有用的工具。     

具有还原性的多重多维模糊推理算法

Zadeh提出的CRI算法是模糊推理中运用最广泛的算法，但它不具还原性。王国俊教授提出的全蕴涵三Ⅰ算法，以及已有的基于相似度的模糊推理算法是在单一规则下的模糊推理算法，在单一规则情形满足还原性条件。本文在给出多重多维模糊推理具有还原性的定义的基础上，先用例子说明，全蕴涵三Ⅰ算法和已有的基于相似度的算法在多重多维推理的情况下不具有还原性，同时本文提出一种新的基于相似度的模糊推理算法，在多重多维推理时满足还原性条件。     

基于余弦贴近度与群体共识度的正态云多准则群决策方法

对于正态云多准则群决策问题,提出一种基于余弦贴近度与群体共识度的决策方法. 将各专家的正态云决策矩阵转化为泛型正态随机决策矩阵, 利用正态云的线性组合方法, 计算出线性加权泛型正态随机变量. 基于理想方案, 求得各专家认为各方案的余弦相似度、余弦贴近度和排序结果. 通过群体共识度, 判断专家们决策意见的一致性程度, 求得各方案的加权的余弦相似度、余弦贴近度, 并得到最终排序. 最后, 通过算例验证了所提出方法的可行性和有效性.     

基于OWA算子的区间值加权模糊推理

针对如何对区间值模糊产生式规则赋予合理权值的问题,将OWA算子引入到区间值模糊推理中。介绍一种基于OWA算子的区间值赋权方法,根据此方法给出区间值模糊集上的加权模糊产生式规则的推理算法。在采用该算法的过程中,为合理地计算输入事实与规则前件的匹配程度,引入基于OWA算子的区间值模糊匹配函数值和总体贴近度的计算方法。实例分析表明了所给出的区间值模糊推理算法的有效性和可行性。     

一种改进的基于相似度的模糊推理方法

基于模糊集合概念的模糊产生式规则及其模糊推理方法广泛应用于智能控制、专家系统等领域。文章指出了现有的一种基于相似度的模糊推理方法的缺陷。提出了一种改进的基于相似度的模糊推理方法。并通过实例说明了改进方法的优越性。     

基于直觉模糊推理的多属性群决策方法研究*

针对属性取值以直觉模糊数形式给出的多属性决策问题,提出了基于直觉模糊推理的多属性群决策方法。首先针对专家的评价信息构建直觉决策推理规则,然后根据规则之间的关系给出了决策推理模型,进而给出了基于直觉模糊推理的决策方法;最后通过购房实例验证了该方法的正确性和有效性。     

直觉模糊推理机的设计及其推理算子选择研究*

首先引入直觉模糊集下的模糊蕴涵算子运算方法,设计应用直觉模糊蕴涵算子的直觉模糊推理机模型的设计,阐述模型的运行机理,通过实例验证直觉模糊推理机模型的有效性和正确性.提出选择模糊蕴涵算子的贴近度法,在具体应用领域中可根据比较结果作具体选择.     

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

Surveillance multisensor management with fuzzy evaluation of sensor task priorities

In this contribution a fuzzy management scheme is proposed to cope with the evaluation of multisensor tasks priority in defence surveillance applications. Based on all fused track and sector data, a reasoning system determines the priority of each surveillance task to perform during the decision cycle, by means of a symbolic inference process inspired in the behaviour of human operators. This approach allows to integrate high-level information (possibly subjective concepts, considering also their uncertainty) with conventional numeric representations in the decision process. The elected formal method to represent the variables involved in this decision process is the theory of possibility and fuzzy sets, since it offers a unified framework to represent uncertainty knowledge. In this sense, to obtain the priority for each task, the reasoning process relies on a decision tree whose nodes are linguistic variables representing intermediate concepts used by a human operator to determine the tasks priorities. The validity of the fuzzy reasoning approach is supported by the fact that it has been able to manage environmental situations in a similar way as experienced human operators do. Included results illustrate how the importance of the tasks, measured through their time-varying priorities, allows the manager to timely adapt sensor operation to changing situations.     

Fuzzy multiattribute group decision making based on intuitionistic fuzzy sets and evidential reasoning methodology

In this paper, we propose a new fuzzy multiattribute group decision making method based on intuitionistic fuzzy sets and the evidential reasoning methodology. First, the proposed method uses the evidential reasoning methodology to aggregate each decision maker’s decision matrix and the weights of the attributes to get the aggregated decision matrix of each decision maker. Then, it uses the obtained aggregated decision matrices of the experts, the weights of the experts and the evidential reasoning methodology to get the aggregated intuitionistic fuzzy value of each alternative. Finally, it calculates the transformed value of the obtained intuitionistic fuzzy value of each alternative. The smaller the transformed value, the better the preference order of the alternative. The proposed method can overcome the drawbacks of the existing methods for fuzzy multiattribute group decision making in intuitionistic fuzzy environments.     

A fuzzy reasoning design for fault detection and diagnosis of a computer-controlled system

A fuzzy reasoning and verification Petri nets (FRVPNs) model is established for an error detection and diagnosis mechanism applied to a complex fault-tolerant PC-controlled system. The inference accuracy can be improved through the hierarchical design of a two-level fuzzy rule decision tree and a Petri nets technique to transform the fuzzy rule into the FRVPNs model. Several simulation examples of the assumed failure events were carried out by using the FRVPNs and the Mamdani fuzzy method with MATLAB tools. The reasoning performance of the developed FRVPNs was verified by comparing the inference outcome to that of the Mamdani method. Both methods result in the same conclusions. Thus, the present study demonstrates that the proposed FRVPNs model is able to achieve the purpose of reasoning, and furthermore, determining of the failure event of the monitored application program.     

模糊数学在企业信息处理中的应用--模糊人事评价及人事安排、岗位培训决策支持系统

本文讨论了模糊数学在企业信息处理中的应用。提出了模糊人事评价体系，以及企业人事安排、企业员工技能培训等方面的模糊决策支持数学模型。     

A novel approach to guarantee good robustness of fuzzy reasoning

The analysis of internal connective operators of fuzzy reasoning is very significant and the robustness of fuzzy reasoning has been calling for study. An interesting and important question is that, how to choose suitable internal connective operators to guarantee good robustness of rule-based fuzzy reasoning? This paper is intended to answer it. In this paper, Lipschitz aggregation property and copula characteristic of t-norms and implications are discussed. The robustness of rule-based fuzzy reasoning is investigated and the relationships among input perturbation, rule perturbation and output perturbation are presented. The suitable t-norm and implication can be chosen to satisfy the need of robustness of fuzzy reasoning. In 1-Lipschitz operators, if both t-norm and implication are copulas, the rule-based fuzzy reasoning is much more stable and more reliable. In copulas, if both t-norm and implication are 1-l_∞-Lipschitz, they can guarantee good robustness of fuzzy reasoning. The experiments not only illustrate the ideas proposed in the paper but also can be regarded as applications of soft computing. The approach in the paper also provides guidance for choosing suitable fuzzy connective operators and decision making application in rule-based fuzzy reasoning.     

交通拥挤控制的实时决策支持模型

针对交通拥挤的快速检测和控制问题,设计了一种基于管理者知识和经验,能够实时反映交通拥挤状况的决策支持模型．设计的自动决策支持模型中包含了概念模型和推理模型,推理模型中集成了交通拥挤自动检测算法和拥挤交通量控制方案选择的模糊推理算法．研究结果表明,采用自动决策支持模型能在一定程度上取代交通管理者对拥挤的管理,从而提高拥挤管理的效率和决策质量．     

A fuzzy logic-based system for assessing the level of business-to-consumer (B2C) trust in electronic commerce

The purpose of this paper is to present an application of fuzzy logic to human reasoning about electronic commerce (e-commerce) transactions. This paper uncovers some of the hidden relationships between critical factors such as security, familiarity, design, and competitiveness. We analyze the effect of these factors on human decision process and how they affect the Business-to-Consumer (B2C) outcome when they are used collectively. This research provides a toolset for B2C vendors to access and evaluate a user's transaction decision process, and also an assisted reasoning tool for the online user.     

基于动态模糊综合评判的故障预测方法

根据故障预测的特点和要求,指出传统模糊综合评判方法的不足,提出了动态模糊综合评判方法,给出了动态权重矩阵和动态模糊关系矩阵的确定方法。并与规则推理相结合,以反后座装置为例对评判过程予以说明,证明了该方法的有效性和实用性。