A weight adaptation method for fuzzy cognitive map learning

Fuzzy cognitive maps (FCMs) constitute an attractive modeling approach that encompasses advantageous features. The most pronounces are the flexibility in system design, model and control, the comprehensive operation and the abstractive representation of complex systems. The main deficiencies of FCMs are the critical dependence on the initial experts beliefs, the recalculation of the weights corresponding to each concept every time a new strategy is adopted and the potential convergence to undesired equilibrium states. In order to update the initial knowledge of human experts and to combine the human experts structural knowledge with the training from data, a learning methodology for FCMs is proposed. This learning method, based on nonlinear Hebbian-type learning algorithm, is used to adapt the cause–effect relationships of the FCM model improving the efficiency and robustness of FCMs. A process control problem is presented and its process is investigated using the proposed weight adaptation technique.     

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     

Ranking fuzzy cognitive map based scenarios with TOPSIS

Scenarios describe events and situations that would occurred in the future real-world. Policy makers use scenario methods as a tool to build landscapes of possible futures at a national level. Based on these future visions, policy and decision-makers are able to explore different courses of action. In recent years, the number of potential scenario methods and applications is increasing. It is because academics and practitioners are increasing their interest about it. In spite of the success of scenario methods’ support, scenario-based decision making still is not a fully structured process.The proposed methodology aims to bring methodological support to scenario-based decision making in scenario analysis. The originality of the proposed approach with respect to other ones is that it aims to use the scenarios’ assessment and ranking as a whole. Traditional approaches consider the future impact of each present entity in isolation. This assumption is a simplification of a more complex reality, in which different entities interact with each other. The model that the authors propose allows decision and policy makers to measure the impact of a entity interactions. To reach this aim, the proposal combine Delphi method, soft computing (fuzzy cognitive maps) and multicriteria (TOPSIS) techniques. In addition, a numerical example is developed for illustrating the proposal.     

A fuzzy cognitive map approach for effect-based operations: An illustrative case

This paper proposes the use of fuzzy cognitive maps (FCMs) as a technique for supporting the decision-making process in effect-based planning. The goal is to determine alternative courses of action to realize the aims of an operation, and choose the best option among them. With adequate consideration of the problem features and the constraints governing the method used, an FCM is developed to model effect-based operations (EBOs). In this study, certain features that do not exist in the classical FCM method were added to our FCM concept value calculation algorithm; these include influence possibility, influence duration, dynamic influence value-changing, and influence permanence. The model developed was applied to an illustrative scenario involving military planning, and we comment on the usefulness of the proposed methodology.     

基于测度递进的模糊认知图及其应用

模糊认知图(fuzzy cognitive map,FCM)具有简单的推理机制和较强的因果关系表达能力,已得到广泛关注和研究,但FCM对专家经验知识具有较强的依赖性,故而限制了在复杂动态系统建模中的应用.基于此,提出了一种测度递进策略的模糊认知图学习方法.利用线性回归算法,学习得到模糊认知图权重矩阵粗模型;将神经网络的权值调整算法应用于权重矩阵粗模型的细化过程,将该模糊认知图模型应用在股票市场,实现对股票日均值的预测.实验结果表明了该建模方式是有效的.     

Reasoning and unsupervised learning in a fuzzy cognitive map

This paper presents a new model for unsupervised learning and reasoning on a special type of cognitive maps realized with Petri nets. The unsupervised learning process in the present context adapts the weights of the directed arcs from transitions to places in the Petri net. A Hebbian-type learning algorithm with a natural decay in weights is employed to study the dynamic behavior of the algorithm. The algorithm is conditionally stable for a suitable range of the mortality rate. After convergence of the learning algorithm, the network may be used for computing the beliefs of the desired propositions from the supplied beliefs of the axioms (places with no input arcs). Because of the conditional stability of the algorithm, it may be used in complex decision-making and learning such as automated car driving in an accident-prone environment. The paper also presents a new model for knowledge refinement by adaptation of weights in a fuzzy Petri net using a different form of Hebbian learning. This second model converges to stable points in both encoding and recall phases.     

A fuzzy cognitive map model to calculate a user's desirability based on personality in e-learning environments

The recent research in artificial intelligence shows an increasing interest in the modeling of human behavior factors such as personality, mood, and emotion for developing human-friendly systems. That is why there is an interest in developing models and algorithms to determine a human's emotions while interacting with a system to improve the quality of the interaction. In this paper, we propose a computational model to calculate a user's desirability based on personality in e-learning environments. The desirability is one of the most important variables in determining a user's emotions. The model receives several e-learning environmental events and predicts the desirability of the events based on the user's personality and his/her goals. The proposed model has been evaluated in a simulated and real e-learning environment. The results show that the model formulates the relationship between personality and emotions with high accuracy.     

Contextual fuzzy cognitive map for decision support in geographicinformation systems

Fuzzy cognitive maps or FCMs have been shown to be useful when representing qualitative data. We have shown that these FCM structures can be used to represent quantitative and qualitative data. We illustrate this structure applied to geographic information system (GIS) applications. We illustrate the types of CFCMs we can generate using real census data, human expert knowledge, and quantitative data in the form of maps in a GIS. The goal of this system is to use objects (topographical and conceptual) and their relationships, either supplied by census data or generated by the GIS and to map them as layers in the GIS. Using fuzzy membership functions from experiments with GIS users, we can construct CFCMs for decision support. This will also have significant applications in intelligent servants that are able to assist and interact with the human user     

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.     

A hybrid fuzzy regression-fuzzy cognitive map algorithm for forecasting and optimization of housing market fluctuations

This paper presents a hybrid algorithm based on fuzzy linear regression (FLR) and fuzzy cognitive map (FCM) to deal with the problem of forecasting and optimization of housing market fluctuations. Due to the uncertainty and severe noise associated with the housing market, the application of crisp data for forecasting and optimization purposes is insufficient. Hence, in order to enable the decision-makers to make decisions with respect to imprecise/fuzzy data, FLR is used in the proposed hybrid algorithm. The best-fitted FLR model is then selected with respect to two indicators including Index of Confidence (IC) and Mean Absolute Percentage Error (MAPE). To achieve this objective, analysis of variance (ANOVA) for a randomized complete block design (RCBD) is employed. The primary objective of this study is to utilize imprecise/fuzzy data in order to improve the analysis of housing price fluctuations, in accordance with the factors obtained through the best-fitted FLR model. The secondary objective of this study is the exhibition of the resulted values in a schematic way via FCM. Hybridization of FLR and FCM provides a decision support system (DSS) for utilization of historical data to predict housing market fluctuation in the future and identify the influence of the other parameters. The proposed hybrid FLR-FCM algorithm enables the decision-makers to utilize imprecise and ambiguous data and represent the resulted values of the model more clearly. This is the first study that utilizes a hybrid intelligent approach for housing price and market forecasting and optimization.     

六西格玛方法在过程改造中的应用简介

介绍了六西格玛(6σ)的概念、基本评价指标及其开发的DMAIC流程的含义与主要分析工具,并介绍了六西格玛法在过程优化中的应用。该过程是一生产己内酰胺的连续过程,6σ利用统计学工具如量规分析、方差分析对过程的各个生产单元进行分析,找出各个生产单元的关键输入变量和输出变量及其关系,确定影响产品产量的瓶颈,包括操作条件和低效或陈旧的仪表与设备,并用实验设计验证以上分析结果,最后消除瓶颈,达到提高产量的目的。     

A blog-based dynamic learning map

Problem-based learning is a goal directed and constructive process for learners. When meeting problems, learners usually force themselves to form work groups in order to find a solution. Currently, blogs are becoming more popular and in fact has formed a community wherein people can share their learning experiences with others. Many pedagogical applications have adopted what are posted in the community for supplementary learning. Integrating blogs in an intelligent tutoring system means that learners can better regulate and enhance their own learning. In this study, a novel learning device, a blog-based dynamic learning map, which employs both information retrieval and automated scheduling techniques, is designed to provide useful blog articles to help learning. The relevant articles in blogs are used to promote learner engagement in their interactions with the learning map and hence achieve their goals more easily. An experimental course has been implemented and the results show that learners make use of the blog-based learning aid in a very positive way and can eventually cross the specified threshold in a test. The proposed approach can encapsulate the dynamic learning principles in cohesive and supportive ways. Thus it can lead learners to gain useful supplementary materials, shorten the learning time and offering expanded alternative viewpoints to use in the solution of assigned problems. Our results show that both the learners and lectures are very positive to the design of our blog-based dynamic learning map.     

A fuzzy MHT algorithm applied to text-based information tracking

We carry out a detailed analysis of a fuzzy version of Reid's classical multiple hypothesis tracking (MHT) algorithm. Our fuzzy version is based on well-known fuzzy feedback systems, but the fact that the system we describe is specialized for likelihood discrimination makes this study particularly novel. We discuss several techniques for rule activation. One of them, namely the sum-product, seems particularly useful for likelihood management and its linearity makes it tractable for further analysis. Our analysis is performed in two stages: 1) we demonstrate that, with appropriately chosen rules, our system can discriminate the correct hypothesis; and 2) the steady-state behavior with a constant input is characterized analytically. This enables us to establish the optimality of the sum-product method and it also gives a simple procedure to predict the system's behavior as a function of the rule base. We believe this fact can be used to devise a simple procedure for fine-tuning the rule base according to the system designer's needs. The application driving our fuzzy MHT implementation and analysis is the tracking of natural language text-based messages. This application is used as an example throughout the paper     

A reader for the cognitive map

A local diffusion model (Staddon and Reid, 1990) can reproduce exponential and Gaussian stimulus-generalization gradients. We show that a two-dimensional diffusion model, together with simple reinforcement assumptions, can reproduce many of the empirical properties of goal-directed spatial search, including area-restricted search, open-field foraging, barrier and detour problems, maze learning and spatial “insight.” The model provides a simple, associationistic “reader” for Tolman's cognitive map.     

Some models of qualitative dynamic cognitive maps with fuzzy states

Some models of dynamic cognitive maps, whose factors are determined in finite linearly ordered qualitative scales, are studied. Notions of fuzzy values and increments of factors and operations over them are determined. Specific features of defuzzification of fuzzy qualitative values are discussed. Basic behavior effects of these models, sources and forms of data fuzziness in the computing process, means for controlling this event, and confidence limits in the simulating process are studied.     

一种支持模糊查询的LINQ动态查询方案

LINQ（.NET Language Integrated Query）已经成为.NET Framework下的一项主流技术,但运用其动态查询的人却很少,因为目前的几种实现技术都存在各自的缺陷,为此,给出了一种支持模糊查询的LINQ动态查询方案。该方案在遵循LINQ规范的前提下,避免了使用复杂、易受注入攻击、不支持模糊查询等缺陷。该方案与查询对象及其属性无关,因此是真正的动态查询方案。     

Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map

Fuzzy cognitive maps (FCMs) are one of the representative techniques in developing scenarios that include future concepts and issues, as well as their causal relationships. The technique, initially dependent on deductive modeling of expert knowledge, suffered from inherent limitations of scope and subjectivity; though this lack has been partially addressed by the recent emergence of inductive modeling, the fact that inductive modeling uses a retrospective, historical data that often misses trend-breaking developments. Addressing this issue, the paper suggests the utilization of futuristic data, a collection of future-oriented opinions extracted from online communities of large participation, in scenario building. Because futuristic data is both large in scope and prospective in nature, we believe a methodology based on this particular data set addresses problems of subjectivity and myopia suffered by the previous modeling techniques. To this end, text mining (TM) and latent semantic analysis (LSA) algorithm are applied to extract scenario concepts from futuristic data in textual documents; and fuzzy association rule mining (FARM) technique is utilized to identify their causal weights based on if-then rules. To illustrate the utility of proposed approach, a case of electric vehicle is conducted. The suggested approach can improve the effectiveness and efficiency of scanning knowledge for scenario development.     

基于相似粗糙集和模糊认知图的文本分类研究

阐述了基于相似粗糙集和模糊认知图的文本分类问题,提出了一种基于模糊认知图的文本分类推理算法,使文本分类成为一个基于文本特征项的权和特征项与类别的相关度构成的模糊认知图进行推理的结果,最后对该算法进行了实验,并对结果进行了分析.