A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps

In the aluminum reduction process, aluminum uoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic ef ciency. Making the decision on the amount of AlF3 addi- tion (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into con- sideration a variety of interrelated functions;in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is dif cult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have dif culty covering these complex causalities. In this work, a data and knowl- edge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy deci- sion trees, which are used to amend the initial structure provided by experts. The state transition algo- rithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.

A situation awareness assessment method based on fuzzy cognitive maps

The status of an operator’s situation awareness is one of the critical factors that influence the quality of the missions.Thus the measurement method of the situation awareness status is an important topic to research.So far,there are lots of methods designed for the measurement of situation awareness status,but there is no model that can measure it accurately in real-time,so this work is conducted to deal with such a gap.Firstly,collect the relevant physiological data of operators while they are performing a specific mission,simultaneously,measure their status of situation awareness by using the situation awareness global assessment technique(SAGAT),which is known for accuracy but cannot be used in real-time.And then,after the preprocessing of the raw data,use the physiological data as features,the SAGAT’s results as a label to train a fuzzy cognitive map(FCM),which is an explainable and powerful intelligent model.Also,a hybrid learning algorithm of particle swarm optimization(PSO)and gradient descent is proposed for the FCM training.The final results show that the learned FCM can assess the status of situation awareness accurately in real-time,and the proposed hybrid learning algorithm has better efficiency and accuracy.

Using fuzzy cognitive maps to model performance measurement system of Internet-based supply chain

Fuzzy cognitive maps （FCM） is a well-established artificial intelligence technique, which can be effectively applied in the domains of performance measurement, decision making and other management science. FCM can be a useful tool in a group decision-making environment by using scientifically integrated expert knowledge. The theories of FCM and balance scorecard （BSC） both emphasize cause-and-effect relationships among indicators in a complex system, but few reports have been published addressing the combined application of these two techniques. In this paper we propose a FCM simulation model for the sample performance measurement system of Intemet-based supply chain, which is constructed by BSC theory. We gave examples to explain how FCM can be adapted to execute the causal mechanism of BSC, and also how FCM can support group decision-making and forecasting in performance measurement.

Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification

Accurate soil prediction is a vital parameter involved to decide appro-priate crop,which is commonly carried out by the farmers.Designing an auto-mated 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 classifica-tion(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 exam-ining the enhanced soil classification outcomes of the FCMCSO-ASC technique,a series of simulations were carried out on benchmark dataset and the experimen-tal outcomes reported the enhanced performance of the FCMCSO-ASC technique over the recent techniques with maximum accuracy of 96.84%.

A New Control Strategy for Modeling Wind Energy Systems Using Fuzzy Cognitive Maps

Wind energy is currently a fast-growing interdisciplinary field that encompasses many different branches of engineering and science. Modeling and controlling wind energy systems are difficult and challenging problems. The basic structure of wind turbines and some wind control system methods are briefly reviewed. The need for using advanced theories from fuzzy and intelligent systems in studying wind energy systems is identified and justified. FCMs （fuzzy cognitive maps） are used to model wind energy systems. Simulation studies are performed and obtained results are discussed. A new mathematical approach has been proposed to model dynamical complex systems, the DYFUKN （dynamic fuzzy knowledge networks）. Many open problems in the areas of modeling and controlling wind energy systems are outlined.

Application of fuzzy cognitive map in information intelligent push

Since computer system functions are becoming increasingly complex, the user has to spend much more time on the process of seeking information, instead of utilizing the required infor- mation. Information intelligent push technology could replace the traditional method to speed up the information retrieval process. The fuzzy cognitive map has strong knowledge representation ability and reasoning capability. Information intelligent push with the basis on fuzzy cognitive map could ab- stract the computer user＇ s operations to a fuzzy cognitive map, and infer the user＇ s operating inten- tions. The reasoning results will be translated into operational events, and drive the computer system to push appropriate information to the user.

A Fuzzy Expert System Architecture for Intelligent Tutoring Systems:A Cognitive Mapping Approach

An Intelligent Tutoring System (ITS) is a computer based instruction tool that attempts to provide individualized instructions based on learner’s educational status. Advances in development of these systems have rose and fell since their emergence. Perhaps the main reason for this is the absence of appropriate framework for ITS development. This paper proposes a framework for designing two main parts of ITSs. Besides development framework, the second main reason for lack of significant advances in ITS development is its development cost. In general, this cost for instructional material is quite high and it becomes more in ITS development. The proposed method can significantly reduce the development cost. The cost reduction mainly is because of characteristics of applied mapping techniques. These maps are human readable and easily understandable by people who are not aware of knowledge representation techniques. The proposed framework is implemented for a graduate course at a technical university in Asia. This experiment provides an individualized instruction which is the main designing purpose of the ITSs.

Adaptive level of autonomy for human-UAVs collaborative surveillance using situated fuzzy cognitive maps

Collaborating with a squad of Unmanned Aerial Vehicles(UAVs)is challenging for a human operator in a cooperative surveillance task.In this paper,we propose a cognitive model that can dynamically adjust the Levels of Autonomy(LOA)of the human-UAVs team according to the changes in task complexity and human cognitive states.Specifically,we use the Situated Fuzzy Cognitive Map(Si FCM)to model the relations among tasks,situations,human states and LOA.A recurrent structure has been used to learn the strategy of adjusting the LOA,while the collaboration task is separated into a perception routine and a control routine.Experiment results have shown that the workload of the human operator is well balanced with the task efficiency.

Medical decision support systems based on Fuzzy Cognitive Maps

This paper determines the risk for cardiovascular diseases(CVDs).and nutrition level in infants aged 06 montlis using Fuzzy Cognitive Maps(FCMs).The aim of this study is to facilitates the medical experts to early detects these diseases with accuracy,so that overall death ratio can be reduced.Firstly,we have introduced the concepts of FCMs and briefly refer to the applications of these methods in medical.After that,two intel ligent decision support systems for cardiovascular and malnutrition are developed using FCMs.The proposed cardiovascular risk assessment system takes six inputs:chest pain,cholesterol,heart rate,blood pressure,blood sugar,and old peak and determines CVDs risk.The second decision support system of malnutrition diagnosis takes twelve inputs:breastfeeding,daily income,maternal education,colostrum intake,energy intake,protein intake,vitamin A intake,iron intake,family size,height,weight,head circumference,and skin fold thickness and diagnoses the nutrition level in infants.We have explained the working of both decision support systems using case studies.

Cooperative Threat Assessment of Multi-aircrafts Based on Synthetic Fuzzy Cognitive Map

Threat assessment is one of the most important parts of the tactical decisions,and it has a very important influence on task allocation.An application of fuzzy cognitive map(FCM) for target threat assessment in the air combat is introduced.Considering the fact that the aircrafts participated in the cooperation may not have the same threat assessment mechanism,two different FCM models are established.Using the method of combination,the model of cooperative threat assessment in air combat of multi-aircrafts is established.Simulation results show preliminarily that the method is reasonable and effective.Using FCM for threat assessment is feasible.

Unsupervised Dynamic Fuzzy Cognitive Map

Fuzzy Cognitive Map （FCM） is an inference network, which uses cyclic digraphs for knowledge representation and reasoning. Along with the extensive applications of FCMs, there are some limitations that emerge due to the deficiencies associated with FCM itself. In order to eliminate these deficiencies, we propose an unsupervised dynamic fuzzy cognitive map using behaviors and nonlinear relationships. In this model, we introduce dynamic weights and trend-effects to make the model more reasonable. Data credibility is also considered while establishing a machine learning model. Subsequently, we develop an optimized Estimation of Distribution Algorithm （EDA） for weight learning. Experimental results show the practicability of the dynamic FCM model. In comparison to the other existing algorithms, the proposed algorithm has better performance in terms of convergence and stability.

A fuzzy cognitive map-based algorithm for predicting water consumption in Spanish healthcare centres

The management of water consumption in healthcare centres can have positive impacts on both the environmental performance and profitability of health systems.Computational tools assist in the decision-making process of managing the operation and maintenance of healthcare centres.This research aimed to integrate the empirical knowledge of experts in Healthcare Engineering and the historical data from 66 healthcare centres in a Fuzzy Cognitive Map.The outputs of the predictive model included water consumption,water cost,and CO_(2) emissions in healthcare facilities,along with eleven variables to discover the causes and consequences of water consumption in healthcare centres.A healthcare centre with about 12350 users,located in a city that experiences an average of 1100 heating degree days,whose facilities be moderately energy-efficient contributing over 50%with renewable energies is expected to consume 8.4 dam^(3) of water with 32.1 k€of cost,and contribute realising 30.8 ton CO_(2)eq emissions.The use of Fuzzy Cognitive Maps for prediction can provide a high level of effectiveness in identifying the factors that contribute to water consumption and in designing key performance indicators to manage the environmental performance of healthcare buildings.This tool is extremely effective in enhancing the performance of the management division of health systems.

An Intelligent Medical Expert System Using Temporal Fuzzy Rules and Neural Classifier

As per World Health Organization report which was released in the year of 2019,Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world.Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it.Among the diabetics,it can be observed that a large number of people had failed to identify their disease in the initial stage itself and hence the disease level moved from Type-1 to Type-2.To avoid this situation,we propose a new fuzzy logic based neural classifier for early detection of diabetes.A set of new neuro-fuzzy rules is introduced with time constraints that are applied for thefirst level classification.These levels are further refined by using the Fuzzy Cognitive Maps(FCM)with time intervals for making thefinal decision over the classification process.The main objective of this proposed model is to detect the diabetes level based on the time.Also,the set of neuro-fuzzy rules are used for selecting the most contributing values over the decision-making process in diabetes prediction.The proposed model proved its efficiency in performance after experiments conducted not only from the repository but also by using the standard diabetic detection models that are available in the market.

DEMATEL和模糊认知图在地铁深基坑施工安全风险动态评估中的应用

为探究地铁深基坑施工安全风险因素动态作用规律,科学预防施工安全事故,针对目前缺乏因素系统识别、因果关系量化和动态推理分析等共性问题,提出一种基于决策试验和评估实验室(Decision Making Trial and Evaluation Laboratory DEMATEL)与模糊认知图(Fuzzy Cognitive Map,FCM)的地铁深基坑施工安全风险分析方法。首先,通过理论分析与文献梳理,采用扎根理论识别风险因素;其次,结合专家调研与量化分析,利用DEMATEL方法分析风险因素的因果关系;再次,将DEMATAL决策矩阵转化为FCM模型的交互作用矩阵,展开风险因素的预测与诊断推理分析;最后,选取案例进行实证,验证模型方法的可用性与有效性。结果显示:因素X_(1)(人员安全风险意识)对其他因素的影响程度最高;因素X_(1)(人员安全风险意识)、X_(8)(安全施工组织设计方案)和X_(7)(施工安全风险管理措施)是排名前3的关键风险因素;完善安全施工组织设计方案是最有效的管控对策。

基于模糊认知图的高铁企业国际声誉形成机制仿真分析

为探究高铁企业国际声誉形成机制的动态演化过程,采用一种结合了结构方程模型和模糊认知图的方法进行仿真分析。预测分析结果表明:宏观因素、企业能力和被感知的能力均与国际声誉呈正相关,与国际声誉之间的相关性大小排序为宏观因素>企业能力>被感知的能力。诊断分析结果表明:宏观因素是引起国际声誉变化的最根本原因。混合分析结果证明了以宏观因素为核心采取综合干预措施,对国际声誉的提升效果最好。基于分析结果,提出了高铁企业提升国际声誉的对策。这种混合方法模拟了因素间的动态交互作用,并在不同假设情景下进行仿真分析,揭示了高铁企业国际声誉形成机制的动态演化过程。研究结果有助于拓展国际声誉形成机制的知识体系,也可为高铁企业采取针对性策略提升国际声誉提供实践参考。

基于小生境算法的空气质量模糊认知图预测

工业化使得全球经济取得了突飞猛进的增长,但也使得环境污染愈发严重。大气污染成为世界各国讨论的话题。文中提出一种基于小生境遗传算法的改进形式的空气质量模糊认知图预测方法。利用模糊认知图表示空气污染物以及空气质量指数的关联关系,并应用改进的小生境遗传算法优化模型,使得训练结果更接近全局最优解。文中使用2015-2021年的空气数据训练模型,在2022年的数据集上测试模型。实验结果表明,与传统的遗传算法和BP神经网络相比,所提方法预测精度更高,且泛化性能更好,证明了其有效性。

多因素耦合作用下的地铁施工渗漏水风险评价

针对多因素耦合作用下地铁盾构施工渗漏水灾害分析评价的模糊性和动态解释能力不足的问题,基于人、物、法、环4个维度构建地铁施工渗漏水风险评价指标体系;运用组合数有序加权算子(Combination Ordered Weighted Averaging,C-OWA)削弱主观因素影响建立相互作用矩阵,通过相互作用矩阵与模糊认知图的融合模型识别关键风险、预测演化趋势;在此基础上,基于可拓云物元理论构建了地铁盾构隧道渗漏水风险评价模型,并以某地铁隧道工程为例进行实证分析,结果表明,该盾构隧道施工渗漏水风险状态为安全,与实际情况相符,验证了该方法的可行性与适用性。该模型可为地铁盾构施工渗漏水风险评估与防范提供一种操作性强的方法。

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.

水泥粉磨细度控制研究进展

针对水泥辊压机终粉磨生产过程中产品颗粒过细、粒径分布窄的问题,分析辊压机粉磨数值模拟过程,探究水泥粉磨最佳粒径分布,总结水泥粉磨细度建模、粉磨过程优化和产品细度控制的国内外研究成果,认为通过建立模糊认知图模型可以解决水泥产品粒径分布不均的问题;阐述改进的比例-积分-微分控制策略、专家系统、神经网络、模糊控制等先进控制方法以及基于最优理论的模型参考自适应控制算法,并论证以上方法在水泥辊压机终粉磨过程中应用的可行性,指出模糊认知图与模型参考自适应控制相结合的控制策略可以优化水泥细粉粒径分布,提升水泥产品质量。

装配式建筑供应链韧性对建造成本的影响路径研究

基于装配式建筑供应链韧性定义及表现,提取反映供应链韧性的风险管理能力、协同合作水平、信息化水平、生产能力以及冗余资源五个因素;通过文献梳理筛选出韧性测度指标和成本变量,并构建结构方程模型(SEM)揭示指标之间的路径关系;最后基于模糊认知图(FCM)进行路径演化分析,找出影响成本的关键因素,希望为节约装配式建筑建造成本提供参考。