模糊认知图权值学习法

模糊认知图简单、直观的图形化表示和快捷的数值推理能力使其在医学、工业过程控制以及环境监测等领域得到了广泛的应用.由于受到人的经验、知识水平和认知能力的限制,很难由领域专家直接构建大规模系统的模糊认知图.近年来依据动态数据自动或半自动构建模糊认知图的研究越来越多.模糊认知图的权值学习主要分为基于Hebbian技术、遗传算法、群体智能和最小平方四大类,在此方面学者提出了颇多算法.作者就基于数据进行模糊认知图权值学习的各种方法进行综述、比较和分析,指出各种学习方法的适用性,以便于在实际应用中进行选择.     

Fuzzy Cognitive Maps for stereovision matching

This paper outlines a method for solving the stereovision matching problem using edge segments as the primitives. In stereovision matching the following constraints are commonly used: epipolar, similarity, smoothness, ordering and uniqueness. We propose a new matching strategy under a fuzzy context in which such constraints are mapped. The fuzzy context integrates both Fuzzy Clustering and Fuzzy Cognitive Maps. With such purpose a network of concepts (nodes) is designed, each concept represents a pair of primitives to be matched. Each concept has associated a fuzzy value which determines the degree of the correspondence. The goal is to achieve high performance in terms of correct matches. The main findings of this paper are reflected in the use of the fuzzy context that allows building the network of concepts where the matching constraints are mapped. Initially, each concept value is loaded via the Fuzzy Clustering and then updated by the Fuzzy Cognitive Maps framework. This updating is achieved through the influence of the remainder neighboring concepts until a good global matching solution is achieved. Under this fuzzy approach we gain quantitative and qualitative matching correspondences. This method works as a relaxation matching approach and its performance is illustrated by comparative analysis against some existing global matching methods.     

Fuzzy Grey Cognitive Maps in reliability engineering

Current industrial equipment has become more complex and huge. In this case, the conventional reliability techniques cannot correctly support functional assessment. This paper integrates an innovative soft computing methodology, Fuzzy Grey Cognitive Map (FGCM), into a traditional reliability analysis for better knowledge. FGCMs are used for evaluating, modelling and aiding decision-making by examining causal relations among relevant domain concepts. The proposed procedure is illustrated with a reliability analysis of a transformer active part. Twenty failure causes in the transformer's active part are identified and assessed. In addition, six failure scenarios are simulated. The results revealed the potential of the combination of FGCM and failure analysis for complex systems. The proposed methodology exposes the potential benefits it could provide in order to assist electric power system decision-makers to supply its customer electrical energy with a high degree of reliability.     

Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

Accurate soil prediction is a vital parameter involved to decide appropriate crop, which is commonly carried out by the farmers. Designing an automated soil prediction tool helps to considerably improve the efficacy of the farmers. At the same time, fuzzy logic (FL) approaches can be used for the design of predictive models, particularly, Fuzzy Cognitive Maps (FCMs) have involved the concept of uncertainty representation and cognitive mapping. In other words, the FCM is an integration of the recurrent neural network (RNN) and FL involved in the knowledge engineering phase. In this aspect, this paper introduces effective fuzzy cognitive maps with cat swarm optimization for automated soil classification (FCMCSO-ASC) technique. The goal of the FCMCSO-ASC technique is to identify and categorize seven different types of soil. To accomplish this, the FCMCSO-ASC technique incorporates local diagonal extrema pattern (LDEP) as a feature extractor for producing a collection of feature vectors. In addition, the FCMCSO model is applied for soil classification and the weight values of the FCM model are optimally adjusted by the use of CSO algorithm. For examining the enhanced soil classification outcomes of the FCMCSO-ASC technique, a series of simulations were carried out on benchmark dataset and the experimental outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.     

Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization

This paper introduces a new learning algorithm for Fuzzy Cognitive Maps, which is based on the application of a swarm intelligence algorithm, namely Particle Swarm Optimization. The proposed approach is applied to detect weight matrices that lead the Fuzzy Cognitive Map to desired steady states, thereby refining the initial weight approximation provided by the experts. This is performed through the minimization of a properly defined objective function. This novel method overcomes some deficiencies of other learning algorithms and, thus, improves the efficiency and robustness of Fuzzy Cognitive Maps. The operation of the new method is illustrated on an industrial process control problem, and the obtained simulation results support the claim that it is robust and efficient.     

复杂系统模糊认知图的分解研究

本文对复杂系统的知识表示与推理采用了模糊认知图的分解方法,即对原始认知图的节点进行分组,然后在组节点上构造商认知图。这样关于原始FCM的分析就转化为商认知图和各子FCM的分析,商FCM的因果关系推理提供原始FCM的整体信息,而每个子FCM继承原始FCM的拓扑结构和推理,它提供原始FCM的局部信息。这样降低了复杂系统研究的规模与复杂性,同时也使对复杂系统的研究可以在各个分类层面上和各个分组上进行,增加了模糊认知图概念间研究的丰富性和灵活性。这种分解方法可以根据需要循环往复进行。     

RuleML representation and simulation of Fuzzy Cognitive Maps

Fuzzy Cognitive Map (FCM) technique is a combination of Fuzzy Logic and Artificial Neural Networks that is extensively used by experts and scientists of a diversity of disciplines, for strategic planning, decision making and predictions. A standardized representation of FCMs accompanied by a system that would assist decision makers to simulate their own developed Fuzzy Cognitive Maps would be highly appreciated by them, and would help the dissemination of FCMs. In this paper, (a) a RuleML representation of FCM is proposed and (b) a system is designed and implemented in Prolog programming language to assist experts to simulate their own FCMs. This system returns results in valid RuleML syntax, making them readily available to other cooperative systems. The representation capabilities and the design choices of the implemented system are discussed and a variety of examples are given to demonstrate the use of the system.     

Training Fuzzy Cognitive Maps via Extended Great Deluge Algorithm with applications

Fuzzy Cognitive Map (FCM) is an extension of classical cognitive map (CM). It is mainly a soft computing technique which is used to represent knowledge and causal inference. In order to develop a FCM for a system, a group of experts are usually asked to define concepts or factors that represent the system and describe relations among these concepts. However, in many cases FCM can include subjective factors involved in the determination of FCM weights. Several training (or learning) algorithms are employed in the literature to reduce the subjectivity of the inference so far. In this study, Extended Great Deluge Algorithm (EGDA) has been considered first time in the literature as a training algorithm for FMCs. The performance of the algorithm has been tested with two problems. The first problem is selected from the literature which is a “industrial process control problem”. For this problem the proposed algorithm provided promising results. In the second problem a simulation model of a job shop is developed and utilized in order to investigate causal relationship between the control/performance factors through FCM.     

Risk Analysis: Fuzzy Cognitive Maps Vs Fault Trees

Journal of Computer and Systems Sciences International - Restrictions of the method of fault trees in risk analysis problems are analyzed. As an alternative to this method, we consider the...     

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.     

Using Multilayer Fuzzy Cognitive Maps to diagnose Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is comprised of a group of heterogeneous neurodevelopmental conditions, typically characterized by a triad of symptoms consisting of (1) impaired communication, (2) restricted interests, and (3) repetitive and stereotypical behavior pattern. An accurate and early diagnosis of autism can provide the basis for an appropriate educational and treatment program. In this work, we propose a computational model using a Multilayer Fuzzy Cognitive Map (hereafter referred to as MFCM) based on standardized behavioral assessments diagnosing the ASD (MFCM-ASD). The two standards used in the model are: the Autism Diagnostic Observation Schedule, Second Edition (ADOS2), and the Autism Diagnostic Interview Revised (ADIR). The MFCM’s are a soft computing technique characterized by robust properties that make it an effective technique for medical decision support systems. For the evaluation of the MFCM-ASD model, we have used real datasets of diagnosed cases, so as to compare against other method/approaches. Initial experiments demonstrated that the proposed model outperforms conventional Fuzzy Cognitive Maps (FCMs) for ASD diagnosis. Our MFCM-ASD model serves as a diagnostic tool required to support the medical decisions when determining the correct diagnosis of Autism in children with different cognitive characteristics.     

A multi-method, holistic strategy for researching critical success factors in IT projects

In the field of information systems (IS) there is an observable trend towards the use of multi-method research. Using different research methods allows for the cross-validation of data obtained via multiple approaches, with the potential to increase the robustness of research results. Such a multi-method approach is applicable to a comprehensive research agenda on critical success factors, an agenda that needs to take into account not only the identification, but also the analysis and management of critical success factors. The goal of this article is to contribute new knowledge on how to carry out research on critical success factors in IS projects using a multi-method approach. For this purpose, two research projects are presented, each a variation of the research design customized to particular circumstances. First, there is an outline of the research approach taken for a critical success factor research project in the field of portal implementation, with discussion of the strengths and weaknesses of the project. Taking into consideration these experiences, the research approach of a similar critical success factor research project in the field of offshore software development is then described. Finally, recommendations for using the multi-method research approach in critical success factor research are presented.     

基于概率模糊认知图的入侵检测警报融合机制

提出了一种新型主从式警报融合机制,该机制依据一定的规划,在时间方向上对重复报警进行压缩;在空间方向上借助概率模糊认知图的表达、推理能力,对来自多个检测器的报警进行融合。理论分析和实验信真均表明;该机制能有效地降低虚警和减少重复报警。     

A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps

A novel hybrid method based on evolutionary computation techniques is presented in this paper for training Fuzzy Cognitive Maps. Fuzzy Cognitive Maps is a soft computing technique for modeling complex systems, which combines the synergistic theories of neural networks and fuzzy logic. The methodology of developing Fuzzy Cognitive Maps relies on human expert experience and knowledge, but still exhibits weaknesses in utilization of learning methods and algorithmic background. For this purpose, we investigate a coupling of differential evolution algorithm and unsupervised Hebbian learning algorithm, using both the global search capabilities of Evolutionary strategies and the effectiveness of the nonlinear Hebbian learning rule. The use of differential evolution algorithm is related to the concept of evolution of a number of individuals from generation to generation and that of nonlinear Hebbian rule to the concept of adaptation to the environment by learning. The hybrid algorithm is introduced, presented and applied successfully in real-world problems, from chemical industry and medicine. Experimental results suggest that the hybrid strategy is capable to train FCM effectively leading the system to desired states and determining an appropriate weight matrix for each specific problem.     

Risk assessment for a video surveillance system based on Fuzzy Cognitive Maps

For various IT systems security is considered a key quality factor. In particular, it might be crucial for video surveillance systems, as their goal is to provide continuous protection of critical infrastructure and other facilities. Risk assessment is an important activity in security management; it aims at identifying assets, threats and vulnerabilities, analysis of implemented countermeasures and their effectiveness in mitigating risks. This paper discusses an application of a new risk assessment method, in which risk calculation is based on Fuzzy Cognitive Maps (FCMs) to a complex automated video surveillance system. FCMs are used to capture dependencies between assets and FCM based reasoning is applied to aggregate risks assigned to lower-level assets (e.g. cameras, hardware, software modules, communications, people) to such high level assets as services, maintained data and processes. Lessons learned indicate, that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on effectiveness of security system.     

Adaptive Estimation of Fuzzy Cognitive Maps With Proven Stability and Parameter Convergence

Fuzzy cognitive maps (FCMs) have been introduced by Kosko to model complex behavioral systems in various scientific areas. One issue that has not been adequately studied so far is the conditions under which they reach a certain equilibrium point after an initial perturbation. This is equivalent to studying the existence and uniqueness of solutions for their concept values. In this paper, we study the existence of solutions of FCMs equipped with continuous differentiable sigmoid functions having contractive or, at least, nonexpansive properties. This is done by using an appropriately defined contraction mapping theorem and the nonexpansive mapping theorem. It is proved that when the weight interconnections fulfill certain conditions, the concept values will converge to a unique solution, regardless of the exact values of the initial concept values perturbations, or in some cases, a solution exists that may not necessarily be unique; otherwise, the existence or the uniqueness of equilibrium cannot be assured. Based on these results, an adaptive weight-estimation algorithm is proposed that employs appropriate weight projection criteria to assure that the uniqueness of FCM solution is not compromised. In view of these results, recently proposed extensions of FCM, which are the fuzzy cognitive networks (FCN), are invoked.      

Content presentation personalisation and media adaptation in tourism web sites using Fuzzy Delphi Method and Fuzzy Cognitive Maps

Web sites become more powerful when they can adjust to their users’ needs. Web personalisation refers to adapting both the content and the presentation of web sites, so that to deliver the maximum effect to the user in the most appropriate way. A main objective of web personalisation is to adapt the presentation of the web content in a manner that increases the user’s perceived quality. This paper focuses on the applicability of fuzzy logic techniques to content presentation and media adaptation. More specifically, it applies Fuzzy Delphi Method (FDM) and Fuzzy Cognitive Maps (FCMs) in order to highlight the services features that are most preferred by the customer and to adapt presentation media and layout. Fuzzy logic is utilised to deal with the subjectivity inherent in web design choices and in customers’ perception of services priorities. FDM is used to capture the experts’ knowledge regarding media adaptation with respect to hotel service quality. A prototype that adapts the web site presentation according to customer preferences has been developed and evaluated.