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     

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。     

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。     

Mixed-method integration and advances in fuzzy cognitive maps for computational policy simulations for natural hazard mitigation

Human systems need to be adaptive to the consequences of natural hazards. Public policy decisions on natural hazard mitigation can benefit from computational models that embody a comprehensive view of the system. Such models need to be transparent and integrate both expert and lay expert knowledge and experience in an efficient manner. By integrating hard and soft sciences within an overall systems framework, scientists, policy makers and communities can better understand how to improve adaptive capacity. We present a fuzzy cognitive map based Auto-Associative Neural Networks framework generated from a development mixed method integration (triangulation) for adaptive policy formulations. The specific policies relate to preparation for, response to, and recovery from earthquakes in mountainous ski-field environments – a case study chosen to highlight the framework. Three different data collection techniques – expert geomorphic assessments, semi-structured qualitative interviews with three stakeholder groups (experts and lay experts), and fuzzy cognitive maps (FCM) (node and arc maps of stakeholder perceptions) were employed. FCM were first analysed using Graph theory indices to determine map structure. Special attention was paid to subsequent processing of fuzzy cognitive maps (e.g., condensation and aggregation) with qualitative followed by quantitative means to simplify the FCM from the original total of 300 variables to 5 high-level themes to improve the efficacy of subsequent policy simulations. Specifically, the use of Self Organising Maps (SOM) to group concepts (condensation) and individual stakeholders (aggregation) into social group FCMs is a novel contribution to advancing FCM. In the process, SOM also enabled the embedment of nonlinear relationships inherent in the system in the simplified FCM allowing a platform for realistic and meaningful policy simulations based on collective perceptions. Specifically, each of the three simplified stakeholder group FCM and a total social group FCM was represented by Auto-Associative Neural Networks (AANN) which converts an FCM into a dynamical system that allows policy scenario simulations based on input from both expert and lay expert stakeholders. A policy scenario is the level of importance given to a set of concepts and their effects on the system behaviour as revealed by the simulations. We present the results from one of several policy simulations to highlight the effectiveness of the mixed-method integration leading to simplified-FCM based ANNN simulations. Results revealed the similarities and differences between stakeholder group responses in relation to the scenario analysed and how these formed collective responses in the total social group map. Furthermore, outcomes of group and total social group simulations could be interpreted from individual and group stakeholder FCMs giving credibility to the mixed-method approach.     

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.     

Mapping the influences of resilience engineering on health,safety, and environment and ergonomics management system by using Z‐number cognitive map

This study evaluates and analyzes the impacts of resilience engineering (RE) principles on integrated health, safety, environment, and ergonomics (HSEE) management system. In decision sciences, information should be reliable due to uncertainty and vagueness existing in information. To this end, in this study, the concept of Z‐numbers with fuzzy cognitive map (FCM) approach is integrated and a novel approach named Z‐number cognitive map is proposed. The main advantages of the proposed approach are determination of the weighted causality relations (for employing FCM) as well as handling uncertainty (for considering Z‐numbers concept). This approach is used to show the effects of RE indicators on HSEE management system. The required data for the proposed approach is collected from a large petrochemical plant by distributing questionnaires. According to the results, the RE principles have significant impact on HSEE management system. Top management commitment, learning, preparedness and awareness have the most impacts on environment, health, ergonomics and safety factors, respectively. This is the first study that employs Z‐number cognitive map for evaluating and improving the impacts of RE on HSEE factors in a large petrochemical plant. The proposed approach in this study, can help managers of various safety‐critical systems to improve their performance in terms of HSEE factors using RE concept.     

Behavioral simulation and optimization of generation companies in electricity markets by fuzzy cognitive map

Simulation can be used in a wide range of applications in an electricity market. There are many reasons that market players and regulators are very interested in anticipating the behavior of the market. Behavior of a generation company (GENCO) in electricity market is an important factor that affects the market behavior. Several factors affect the behavior of a GENCO directly and indirectly. In this study, a new approach based on fuzzy cognitive map (FCM) is introduced to model and simulate GENCO’s behavior in the electricity market with respect to profit maximization. FCM helps the decision makers to understand the complex dynamics between a certain strategic goal and the related factors. This paper examines how effective factors affect on a GENCO’s profit. To identify key factors relevant to the goal, a FCM is built and then analyzed. To analyze this problem, two cases as simple FCM and weighted FCM are considered. Simple FCM shows how the determined factors affect on goal. A hidden pattern is obtained by this case. Weighted FCM helps sensitivity analysis of the model. In addition, the weighted FCM is used usefully to clearly measure the composite effects resulting from changes of multiple factors. This application is shown by two different case studies. This is the first study that models and simulates the behavior of GENCO in electricity market with respect to profit maximization.     

一种模糊认知图分类器的研究*

通过使用模糊认知图来模拟分类过程,构造了一种模糊认知图分类器,提出了它的两种模糊认知图分类模型,并在此基础上给出了使用它进行分类的推理机制。实验证明,该方法具有良好的分类性能。     

Fuzzy cognitive map based on structural equation modeling for the design of controls in business-to-consumer e-commerce web-based systems

Security and integrity of business-to-consumer e-commerce web-based systems (ECWS) is becoming a concern among ECWS adopters. The controls for ECWS are classified into controls for system continuity, access controls, communication controls, and informal controls. The control design for ECWS is not well structured and demands understanding of the complex causal relationships among environmental factors (infrastructure, organizational requirements for security), controls, implementation, and performance. In order to aid the design of ECWS controls, the application of a fuzzy cognitive map, ECFCM (EC-control design using a fuzzy cognitive map), was developed. Structural equation modeling was used to identify relevant relationships among the components and indicate their direction and strength. A standardized causal coefficient from structural equation modeling was then used to create a fuzzy cognitive map, through which the state or movement of one control component was shown to have an influence on the state or movement of others. Thus ECFCM provides a practical insight to IS auditors by addressing the applicability of soft approaches in capturing and illustrating the use of FCM in the design of ECWS controls.     

模糊认知图学习算法及应用综述

模糊认知图(Fuzzy cognitive map, FCM)是建立在认知图和模糊集理论上的一类代表性的软计算理论, 兼具神经网络和模糊决策两者的优势, 已成功地应用于复杂系统建模和时间序列分析等众多领域. 学习权重矩阵是基于模糊认知图建模的首要任务, 是模糊认知图研究领域的焦点. 针对这一核心问题, 首先, 全面综述模糊认知图的基本理论框架, 系统地总结近年来模糊认知图的拓展模型. 其次, 归纳、总结和分析模糊认知图学习算法的最新研究进展, 对学习算法进行重新定义和划分, 深度阐述各类学习算法的时间复杂度和优缺点. 然后, 对比分析各类学习算法在不同科学领域的应用特点以及现有的模糊认知图建模软件工具. 最后, 讨论学习算法未来潜在的研究方向和发展趋势.     

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.     

A framework for designing cognitive trajectory controllers using genetically evolved interval type-2 fuzzy cognitive maps

In this paper, we provide a complete framework for the design of genetically evolved cognitive tracking controller based on interval type-2 (IT2) fuzzy cognitive map (FCM). We construct the cognitive controller based on a nonlinear controller by transforming its representation into a FCM. This representation gives the opportunity to prove the stability of the cognitive controller in the framework of nonlinear control theory. Moreover, with the deployment of IT2-fuzzy sets which are known to be capable to handle high level of uncertainty, the proposed cognitive controller has the ability to deal with uncertainty that are encountered in real-time world applications. To accomplish the design of the cognitive controller, we present a systematic approach based on genetic algorithm to optimize its parameters and learn fuzzy rules by extracting them from model space (e.g., a set of rules). Within the paper, all steps in constructing and designing the IT2-FCM-based cognitive controller are presented. We first show the performance improvements of the proposed IT2-FCM-based tracking controller with extensive and comparative simulation results and then with experimental results that were collected on real-world mobile robot. The results clearly show the superiority of proposed cognitive control systems when compared to its conventional and fuzzy controller counterparts. We believe that the proposed genetically evolved design approach of the IT2-FCM-based cognitive controller will provide a bridge between the well-developed cognitive sciences and control theory.     

A fuzzy cognitive maps–petri nets energy management system for autonomous polygeneration microgrids

Autonomous polygeneration microgrids (APM) are a relatively new approach in covering specific needs like power, potable water and fuel for transportation, in remote areas. This approach has been proved to be technically feasible nowadays and even present itself as an economically viable investment. The initial management system built for this approach is a simple ON/OFF supervisor which can make the APM operate, but not in an optimal way. The devices cannot be operated in part load and as a consequence there is little room for optimization. A combined fuzzy cognitive maps (FCMs)–petri nets (PN) approach has been developed for the energy management of such a system. The PN is used as an activator in the fuzzy cognitive map structure so as to enable different FCMs to be activated depending on the state of the microgrid. This combination forms an integrated approach to the energy management of the microgrid. Using this approach considerable optimization in the design and operation of the microgrid is possible. A methodology for simultaneous and interactive optimization of the energy management system along with the sizing of the various devices of the actual microgrid is implemented. A software platform consisting of TRNSYS, TRNOPT and GenOPT software packages was used for simulation and optimization. Particle swarm optimization is applied both for the sizing of the system and the optimization of the FCM weights and PN parameters. Two microgrids were designed, one based on the FCM–PN energy management system (FPEMS) and one on the ON/OFF approach. The results show that FPEMS manages the energy flows more effectively throughout the year which leads to a considerable decrease in the sizing of the various components of the microgrid.     

Design and implementation of a multiagent stock trading system

Stock trading is one of the key items in an economy and estimating its behavior and taking the best decision in it are among the most challenging issues. Solutions based on intelligent agent systems are proposed to cope with those challenges. Agents in a multiagent system (MAS) can share a common goal or they can pursue their own interests. That nature of MASs exactly fits the requirements of a free market economy. Although existing studies include noteworthy proposals on agent‐based market simulation and researchers discuss theoretical design issues of agent‐based stock exchange systems, unfortunately only a very few of the studies consider exact development and implementation of multiagent stock trading systems within the software engineering perspective and guides to the software engineers for constructing such software systems starting from scratch. To fill this gap, in this paper, we discuss the development of a multiagent‐based stock trading system by taking into consideration software design according to a well‐defined agent oriented software engineering methodology and implementation with a widely‐used MAS software development framework. Each participant in the system is first designed as belief–desire–intention agents with their facts, goals, and plans, and then belief–desire–intention reasoning and behavioral structure of the designed agents are implemented. Lessons learned during design and development within the software engineering perspective and evaluation of the implemented multiagent stock exchange system are also reported. Copyright © 2011 John Wiley & Sons, Ltd.     

Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder

The soft computing technique of fuzzy cognitive maps (FCM) for modeling and predicting autistic spectrum disorder has been proposed. The FCM models the behavior of a complex system and is used to develop new knowledge based system applications. FCM combines the robust properties of fuzzy logic and neural networks. To overwhelm the limitations and to improve the efficiency of FCM, a good learning method of unsupervised training could be applied. A decision system based on human knowledge and experience with a FCM trained using unsupervised non-linear hebbian learning algorithm is proposed here. Through this work the hebbian algorithm on non-linear units is used for training FCMs for the autistic disorder prediction problem. The investigated approach serves as a guide in determining the prognosis and in planning the appropriate therapies to special children.     

基于信任知识库的概率模糊认知图

模糊认知图较难表示概念间因果关系测度的不确定性、因果联系的时空特性及专家对知识的不确定性．在继承模糊认知图模型优点的前提下，在概念间的因果关系中引入条件概率及信任知识库表示，提出基于信任知识库的概率模糊认知图模型．该模型用条件概率及信任知识库表示因果联系的时空特性、专家对知识及概念间因果关系测度的不确定性，从而将因果关系测度的不确定性、因果联系的时空特性及专家对知识的不确定性有效地融入模糊认知图中，自然扩展了模糊认知图模拟因果关系的能力，较大限度地减少了认知图对现实世界模拟的失真．最后通过实验说明了基于信任知识库的概率模糊认知图模型，具有比FCM更强的模拟能力．     

Autonomous navigation system using Event Driven-Fuzzy Cognitive Maps

This study developed an autonomous navigation system using Fuzzy Cognitive Maps (FCM). Fuzzy Cognitive Map is a tool that can model qualitative knowledge in a structured way through concepts and causal relationships. Its mathematical representation is based on graph theory. A new variant of FCM, named Event Driven-Fuzzy Cognitive Maps (ED-FCM), is proposed to model decision tasks and/or make inferences in autonomous navigation. The FCM??s arcs are updated from the occurrence of special events as dynamic obstacle detection. As a result, the developed model is able to represent the robot??s dynamic behavior in presence of environment changes. This model skill is achieved by adapting the FCM relationships among concepts. A reinforcement learning algorithm is also used to finely adjust the robot behavior. Some simulation results are discussed highlighting the ability of the autonomous robot to navigate among obstacles (navigation at unknown environment). A fuzzy based navigation system is used as a reference to evaluate the proposed autonomous navigation system performance.     

A dynamic fuzzy cognitive map applied to chemical process supervision

This work develops an intelligent tool based on fuzzy cognitive maps to supervisory process control. Fuzzy cognitive maps are a neuro-fuzzy methodology that can accurate model complexly system using a causal-effect fuzzy reasoning. In the proposed approach, new types of concept and relation, not restricted to cause–effect ones, are added to the model resulting in a dynamic fuzzy cognitive map (D-FCM). In this sense, a supervisory system is developed in order to control a fermentation process. This process has a non-linear behavior and presents several problems, such as non-minimum phase and large accommodation time. The supervisor goal is to operate the process in normal and critical conditions. The expert knowledge about the process behavior in both conditions is used to build the D-FCM supervisor. Simulation results are presented in order to validate the proposed intelligent supervisor.