基于有序加权平均算子的概率模糊认知图

模糊认知图(FCM)与概率模糊认知图(PFCM)使用简单的加权和集结因果推理结果,忽略了原因节点间关联关系的不确定性,阈值函数导致推理结果进一步失真.在继承FCM与PFCM优点的基础上,引入有序加权平均(OWA)算子模拟各种确定的或模糊的与或组合关系,提出了基于有序加权平均算子的概率模糊认知图(OWA-PFCM).通过构建一个动态的攻击效能评估模型,阐述了OWA-PFCM在工程建模中的应用.OWA-PFCM能同时表示因果节点状态的不确定性、因果联系强度的不确定性、与或组合关系的不确定性,具有更强的模拟能力.     

基于概率模糊认知图的混合入侵检测方法

结合模糊认知图理论，构造基于概率模糊认知图（PFCM）的攻击图来描述入侵行为，提出一种结合误用检测和异常检测的基于PFCM的混合入侵检测方法．该方法用模糊概念描述异常，用数值运算代替模式匹配，并利用概率测度有效表示各因素问关系的不确定性．构造基于PFCM的Smurf攻击图并进行检测实验，实验结果表明该方法能在保持高检测率的情况下降低误报率，并具有较好的鲁棒性．     

模糊认知图研究进展

模糊认知图(Fuzzy Cognitive Map,FCM)作为知识表示、推理和软计算方法,通过在传统认知图模型中引入模糊测度来量化概念(concept)间因果关系的影响程度,近年来已成为国内外的研究热点.从研究进展的视角,归纳了FCM的基本框架和推理机制,总结了主流研究中FCM的基本类型,分析了FCM学习算法的主要特征,提出了今后专题研究方向的基本设想,以期对后续研究有所助益.     

模糊认知图研究进展

模糊认知图(Fuzzy Cognitive Map, FCM)作为知识表示、推理和软计算方法,通过在传统认知图模型中引入模糊测度来量化概念(concept)间因果关系的影响程度,近年来已成为国内外的研究热点。从研究进展的视角,归纳了FCM的基本框架和推理机制,总结了主流研究中FCM的基本类型,分析了FCM学习算法的主要特征,提出了今后专题研究方向的基本设想,以期对后续研究有所助益。     

基于模糊认知图的情感Agent模型研究

人工情感是人工心理的一个主要研究内容。从研究人工情感出发,提出一种基于模糊认知图的情感Agent建模的方法。模糊认知图模型通过在传统认知图模型中引入模糊测度来量化概念间因果关系的影响程度。Agent的知识由内部组元的状态以及组元之间的关系权值进行描述,用简单数值运算代替了复杂的符号逻辑来实现Agent的智能推理和决策。通过实验表明,该模型设计简单、易于扩展、适用性好。     

模糊认知图在协同式医疗诊断系统中的应用

针对医疗诊断系统的复杂性,提出了基于模糊认知图的协同式医疗诊断系统模型,它可以合成各个医学专家(Agent)的模糊认知图以清晰地表示各个认知单元间的整个因果关系,并通过关联权值综合算法得出新的具有正反两方面意见的认知图,获得最佳诊断方案。仿真结果表明,基于认知图的协同诊断模型是有效的,由于系统集成了多专家多方面的知识,减少了由于信息缺乏而带来的诊断偏差。     

认知图研究现状与发展趋势

认知图是一种关于因果知识的网络模型,由概念及概念之间的关系组成,可用于鸟瞰智能系统的概念结构.作为智能信息处理的重要工具,它提供了一个有效的软计算方法来支持基于先验知识的自适应行为.本文综述认知图的研究背景,分析和比较古典认知图、模糊认知图、扩展的模糊认知图、神经元认知图、基于规则的模糊认知图等几种重要模型的主要构成及研究现状,探求它们之间的内在联系.并结合作者的研究基础,给出认知图几种可能的发展趋势,以期对该模型的研究与发展起一定的推动作用.     

模糊因果图及在压力容器故障诊断中的应用

因果图理论是一种基于概率论的不确定推理模型，能够进行在线动态推理和对复杂系统进行故障诊断，但在因果图模型中，要求事件的发生概率为是精确值。针对实际情况事件发生概率具有模糊性和不确定性的特点，文中将模糊数引入因果图中，既解决了获取事件发生概率精确值的难度，又使因果图能处理带模糊性和不确定性的问题。并把它用于压力容器故障诊断，结果表明，该法行之有效。     

因果关系,贝叶斯网络与认知图

因果关系在预测和推理中具有重要的作用.贝叶斯网络已被用于构建诊断和决策系 统.近年来模糊认知图得到了重视.模糊认知图为结构性知识与因果推理提供了又一个理论 框架.本文简单介绍贝叶斯网络与认知图及其推理方法在智能系统中的应用.     

基于概率模糊认知图的Mstream攻击检测方法

提出了一种分布式拒绝服务攻击Mstream的检测方法。设计基于概率模糊认知图（PFCM）的Mstream攻击图，并以该图描述Mstream攻击，利用概率测度表述事件间的时序关系、约束关系和关系的不确定性，结合模糊理论设计基于PFCM的Mstream攻击检测算法。实验表明，该方法不但能准确检测出分布式拒绝服务攻击Mstream，而且还能判断出正在进行中的Mstream攻击的程度。     

Fuzzy cognitive map based quality function deployment approach for dishwasher machine selection

The traditional quality function deployment (QFD) approach deals with the weights of customer requirements (CRs), relationships among CRs and design requirements (DRs), correlation among DRs by using crisp values. This paper uses fuzzy numbers to improve the drawbacks of the traditional QFD method because fuzzy numbers enable to make consistent decisions in uncertain environment to decision makers. The existing papers handle a simple multiplication operation to calculate the correlation among CRs and the correlation among DRs. This study proposes fuzzy cognitive map (FCM) approach to calculate these correlations so that FCM is a successful method to handle the interactions among criteria. This paper contributes to the literature by integrating QFD approach and fuzzy cognitive map approach. The weights of DRs are defined in the result of the proposed QFD approach. These weights are used to evaluate the dishwasher machine alternatives in intuitionistic fuzzy VIKOR method. Intuitionistic fuzzy number (IFS) ensures to handle more information than type-1 fuzzy number to describe the fuzziness and the uncertainty of the real life world. Finally, the proposed approach has been implemented to a dishwasher machine selection in order to test its validity.     

Using fuzzy cognitive map for the relationship management in airline service

This paper proposes the usage of fuzzy cognitive map (FCM) for the management of relationships among organizational members in airline service. The main task of relationship management demands consideration of the complex causal relationship among conflict, communication, balanced power, shared values, trust, and cooperation. It is difficult even for experts in organizational behavior to cognitively predict the causal effect of one factor on the others. FCM is used to describe the inference process for the relationship management in airline service. Initially, structural equation models are used for identifying relevant relationships among the factors and indicating their direction and strength. A standardized causal coefficient is then used to create a cognitive map illustrating the effect of the status of one component on the status of another component. The cognitive map provides preliminary insights into the direction of relationship management toward maximizing effectiveness of airline service.     

Dynamical cognitive network - an extension of fuzzy cognitive map

We present the dynamic cognitive network (DCN) which is an extension of the fuzzy cognitive map (FCM). Each concept in the DCNs can have its own value set, depending on how precisely it needs to be described in the network. This enables the DCN to describe the strength of causes and the degree of effects that are crucial to conducting meaningful inferences. The arcs in the DCN define dynamic, causal relationships between concepts. Structurally, DNCs are scalable and more flexible as compared to FCMs. A DCN can be as simple as a cognitive map and FCM, or as complex as a nonlinear dynamic system. To demonstrate the potential applications of DCNs, we present some simulation results. This paper represents our first attempt to develop a dynamic fuzzy inference system using causal relationships. There are many interesting and challenging theoretical and practical issues in DCNs open to further research     

A causal knowledge-based expert system for planning an Internet-based stock trading system

This study describes a causal knowledge-based expert system for planning an Internet-based stock trading system, abbreviated CAKES-ISTS. The case base of this system consists of the qualities that promote ISTS use, two specific facets of ISTS use (stock amount purchased and frequency of use), and user satisfaction. Planning ISTS requires consideration of the complex causal relationships between system qualities, system use, and performance (i.e., user satisfaction). This study also examines the factors affecting the level of system usage and performance. First, this study uses a fuzzy cognitive map (FCM) to develop the causal knowledge base of the expert system for ISTS planning. Second, this study uses structural equation modeling to estimate the relevant relationships among FCM components as well as their direction and strength. Third, this study develops rules based on system qualities to predict the usage and performance level of ISTS, allowing the identification of the qualities essential to enhance system usage and performance. This clearly shows how effective ISTS planning is possible through the inference process provided by CAKES-ISTS.     

无限论域中的粗糙近似空间与信任结构

在粗糙集理论中存在一对近似算子:下近似算子和上近似算子.而在Dempser-Shafer证据理论中有一对对偶的不确定性测度:信任函数与似然函数.集合的下近似和上近似可以看成是对该集合所表示信息的定性描述,而同一集合的信任测度和似然测度可以看成是对该集合的不确定性的定量刻画.针对各种复杂系统中不确定性知识的表示问题,介绍了无限论域中经典和模糊环境下信任结构及其导出的信任函数与似然函数的概念,建立了Dempser-Shafer证据理论中信任函数与似然函数和粗糙集理论中下近似与上近似之间的关系.阐述了由近似空间导出的下近似和上近似的概率生成一对对偶的信任函数和似然函数;反之,对于任何一个信任结构及其生成的信任函数与似然函数,必可以找到一个概率近似空间,使得由近似空间导出的下近似和上近似的概率分别恰好就是所给的信任函数和似然函数.最后,指出了主要理论成果在智能信息系统的知识表示和知识获取方面的潜在应用.     

一种基于非单调逻辑的模型管理方法

本文讨论了模型管理中不确定性的表达、传递、证据合成以及问题求解过程,提出了一种 基于非单调逻辑的模型管理方法:将模型结构形式的不确定性表示为由建模者或领域专家对 问题结构中未知或随机情形所作假设集支持的可能性命题;模型之间不确定性关系的管理通 过对假设环境的真值(一致性)保持和信度调整过程来实现,其依据是在问题求解过程中出现 的冲突情形或者是由决策人提供的有关命题或次判断.     

Uncertainty representation using fuzzy measures

We introduce the fuzzy measure and discuss its use as a unifying structure for modeling knowledge about an uncertain variable. We show that a large class of well-established types of uncertainty representations can be modeled within this framework. A view of the Dempster-Shafer (D-S) belief structure as an uncertainty representation corresponding to a set of possible fuzzy measures is discussed. A methodology for generating this set of fuzzy measures from a belief structure is described. A measure of entropy associated with a fuzzy measure is introduced and its manifestation for different fuzzy measures is described. The problem of uncertain decision making for the case in which the uncertainty represented by a fuzzy measure is considered. The Choquet integral is introduced as providing a generalization of the expected value to this environment.     

Representing partial ignorance

This paper advocates the use of nonpurely probabilistic approaches to higher-order uncertainty. One of the major arguments of Bayesian probability proponents is that representing uncertainty is always decision-driven and as a consequence, uncertainty should be represented by probability. Here we argue that representing partial ignorance is not always decision-driven. Other reasoning tasks such as belief revision for instance are more naturally carried out at the purely cognitive level. Conceiving knowledge representation and decision-making as separate concerns opens the way to nonpurely probabilistic representations of incomplete knowledge. It is pointed out that within a numerical framework, two numbers are needed to account for partial ignorance about events, because on top of truth and falsity, the state of total ignorance must be encoded independently of the number of underlying alternatives. The paper also points out that it is consistent to accept a Bayesian view of decision-making and a non-Bayesian view of knowledge representation because it is possible to map nonprobabilistic degrees of belief to betting probabilities when needed. Conditioning rules in non-Bayesian settings are reviewed, and the difference between focusing on a reference class and revising due to the arrival of new information is pointed out. A comparison of Bayesian and non-Bayesian revision modes is discussed on a classical example