Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

An evaluation method of contribution rate based on fuzzy Bayesian networks for equipment system-of-systems architecture

The contribution rate of equipment system-of-systems architecture(ESoSA)is an important index to evaluate the equipment update,development,and architecture optimization.Since the traditional ESoSA contribution rate evaluation method does not make full use of the fuzzy information and uncertain information in the equipment system-of-systems(ESoS),and the Bayesian network is an effective tool to solve the uncertain information,a new ESoSA contribution rate evaluation method based on the fuzzy Bayesian network(FBN)is proposed.Firstly,based on the operation loop theory,an ESoSA is constructed considering three aspects:reconnaissance equipment,decision equipment,and strike equipment.Next,the fuzzy set theory is introduced to construct the FBN of ESoSA to deal with fuzzy information and uncertain information.Furthermore,the fuzzy importance index of the root node of the FBN is used to calculate the contribution rate of the ESoSA,and the ESoSA contribution rate evaluation model based on the root node fuzzy importance is established.Finally,the feasibility and rationality of this method are validated via an empirical case study of aviation ESoSA.Compared with traditional methods,the evaluation method based on FBN takes various failure states of equipment into consideration,is free of acquiring accurate probability of traditional equipment failure,and models the uncertainty of the relationship between equipment.The proposed method not only supplements and improves the ESoSA contribution rate assessment method,but also broadens the application scope of the Bayesian network.

Multi-hierarchical fuzzy judgment and nested dominance relation of rough set theory-based environmental risk evaluation for tailings reservoirs

Environmental risk assessment of tailings reservoir assessment system is complex and has many index factors.In order to accurately judge surrounding environmental risks of tailings reservoirs and determinate the corresponding prevention and control work,multi-hierarchical fuzzy judgment and nested dominance relation of rough set theory are implemented to evaluate them and find out the rules of this evaluation system with 14 representative cases.The methods of multi-hierarchical fuzzy evaluation can overall consider each influence factor of risk assessment system and their mutual impact,and the index weight based on the analytic hierarchy process is relatively reasonable.Rough set theory based on dominance relation reduces each index attribute from the top down,largely simplifies the complexity of the original evaluation system,and considers the preferential information in each index.Furthermore,grey correlation theory is applied to analysis of importance of each reducted condition attribute.The results demonstrate the feasibility of the proposed safety evaluation system and the application potential.

A Modified Comprehensive Evaluation System of Groundwater Pollution Based on Fuzzy Set Theory

A modified comprehensive evaluation system of groundwater pollution based on Fuzzy set theory is introduced.In this evaluation system,a five-degree membership function and a comprehensive weight function are built by combining Delphi method with double weight method.By studying a typical engineering project,the features and advantages of the modified evaluation system are analyzed by comparing to the popular simple comparison method.This indicates that the weighted average model is applicable in the situation that the content of every evaluation factor is even and the evaluation aim is to externalize the contribution of every evaluation factor in groundwater environmental quality.

Evaluation of the maintenance window scheme using fuzzy soft set theory

The maintenance window scheme(MWS) is one of the most important railway transportation organizational plans and plays an important role in ensuring railway operational safety. However, MWS setting is a very complicated process, and most countries currently do so with the help of a computer-aided decision system. In general, a decision system can generate multiple alternatives for MWS within an acceptable time. Therefore, how to choose the best option from the alternatives is a vital decision. This paper presents a novel framework for MWS evaluation based on the Chinese railway system. Specifically, the requirements of each department related to MWS setting are analysed, and we construct an evaluation indicator system for MWS based on the preferences of different departments. Then, we apply the fuzzy soft set theory to MWS evaluation, a method that not only effectively deals with evaluation of uncertain information, but also gives flexibility for experts to input their subjective judgment.Additionally, using the ‘‘AND’’ operation of soft set theory allows combing evaluation information from multiple evaluators to give comprehensive results. Finally, a case study illustrates the proposed framework, showing that the proposed evaluation indicator system and evaluation method are effective and practical.

Study on the accuracy of comprehensive evaluating method based on fuzzy set theory

The evaluation method and its accuracy for evaluating complex systems are considered. In order to evaluate accurately complex systems, the existed evaluating methods are simply analyzed, and a new comprehensive evaluating method is developed. The new method is integration of Delphi approach, analytic hierarchy process, gray interconnect degree and fuzzy evaluation (DHGF). Its theory foundation is the meta-synthesis methodology from qualitative analysis to quantitative analysis. According to fuzzy set approach, using the methods of concordance of evaluation, redundant verify, double models redundant, and limitations of the method etc, the accuracy of evaluating method of DHGF is estimated, and a practical example is given. The result shows that using the method to evaluate complex system projects is feasible and credible.

New judging model of fuzzy cluster optimal dividing based on rough sets theory

To investigate the judging problem of optimal dividing matrix among several fuzzy dividing matrices in fuzzy dividing space, correspondingly, which is determined by the various choices of cluster samples in the totality sample space, two algorithms are proposed on the basis of the data analysis method in rough sets theory： information system discrete algorithm （algorithm 1） and samples representatives judging algorithm （algorithm 2）. On the principle of the farthest distance, algorithm 1 transforms continuous data into discrete form which could be transacted by rough sets theory. Taking the approximate precision as a criterion, algorithm 2 chooses the sample space with a good representative. Hence, the clustering sample set in inducing and computing optimal dividing matrix can be achieved. Several theorems are proposed to provide strict theoretic foundations for the execution of the algorithm model. An applied example based on the new algorithm model is given, whose result verifies the feasibility of this new algorithm model.

Fuzzy Model for Trust Evaluation

Based on fuzzy set theory, a fuzzy trust model is established by using membership function to describe the fuzziness of trust. The trust vectors of subjective trust are obtained based on a mathematical model of fuzzy synthetic evaluation. Considering the complicated and changeable relationships between various subjects, the multi-level mathematical model of fuzzy synthetic evaluation is introduced. An example of a two-level fuzzy synthetic evaluation model confirms the feasibility of the multi-level fuzzy synthesis evaluation model. The proposed fuzzy model for trust evaluation may provide a promising method for research of trust model in open networks.

Rough Set Fuzzy Optimum Selecting in Multidisciplinary System

Scheme evaluation and selection is an optimum selecting and sequencing problem with multi-objective and multi-level. It can’t follow single objective function or rule. Meanwhile,these objectives are coupled with each other and the attribution information is fuzzy also. It is necessary to find an effective evaluation method which can consider all conditions and restrictions. In this paper,AHP and rough set theory are applied to fuzzy optimization to determine important weight of each attribution. The rough set fuzzy optimum selection is used to eliminate the useless information. Autonomous underwater vehicle (AUV) is large-scale systems with many coupled design variables and objective functions. Their scheme evaluation and selection are very important,which relate to multiple factors,such as reliability; security,service time; the lifecycle,etc. Results of application in torpedo design indicate that this method is feasible.

Novel method for the evaluation of data quality based on fuzzy control

One of the goals of data collection is preparing for decision-making, so high quality requirement must be satisfied. Rational evaluation of data quality is an effective way to identify data problem in time, and the quality of data after this evaluation is satisfactory with the requirement of decision maker. A fuzzy neural network based research method of data quality evaluation is proposed. First, the criteria for the evaluation of data quality are selected to construct the fuzzy sets of evaluating grades, and then by using the learning ability of NN, the objective evaluation of membership is carried out, which can be used for the effective evaluation of data quality. This research has been used in the platform of ＇data report of national compulsory education outlay guarantee＇ from the Chinese Ministry of Education. This method can be used for the effective evaluation of data quality worldwide, and the data quality situation can be found out more completely, objectively, and in better time by using the method.

基于灰色系统理论的电网工程绿色建造模糊综合评价模型

为贯彻落实绿色发展理念,提升现有电网工程绿色建造评价方法的全面性、系统性以及客观性,提出了一种基于灰色系统理论的模糊综合评价模型。通过研究影响电网工程设计、施工与交付等环节的重要绿色评价因子,构建了多层次的绿色建造评价指标体系。并基于灰色系统理论确定指标权重,建立了多层级模糊综合评价模型。同时选取电网工程案例来进行验证,证明了该模型能够科学、合理地确定各指标的权重,从而为电网工程绿色建造评价及不断优化提供了支撑。

基于模糊贝叶斯网络的引水隧洞安全评价模型分析

为准确预测黄河引水隧洞安全隐患,提出基于模糊集理论和主观贝叶斯方法的模糊贝叶斯安全评价模型。结合实际巡检数据,模型使用贝叶斯网络处理模糊信息与不确定信息,将巡检数据中给出的多态性故障事件模糊化;再通过贝叶斯网络的条件概率表找出不同故障事件之间的逻辑关系;最后获得对引水隧洞安全模型的整体评价。通过开展引水隧洞安全评价模型实例分析,验证了该方法的可行性。

基于改进AHP及云模型的水轮机调节系统状态评价

针对水轮机调节系统综合评价不够全面,无法综合反映系统多指标、多层次的复杂性问题,提出了基于模糊层次分析法(AHP)-决策与实验室评估法(DEMATEL)-云模型的水轮机调节系统综合评价模型。首先建立基于主要设备和相关工况的多层次指标体系,采用模糊AHP计算评价指标的初始权重;通过DEMATEL法计算评价指标的相互影响与参照关系,绘制出指标因果关系图,并结合初始权重计算评价指标的综合权重;引入云模型得到评价指标各等级的隶属度矩阵。最后将权重结果与隶属度结合,得到系统综合评价等级。算例分析表明,该方法可高效快捷地对水轮机调节系统运行状态进行客观评价,为状态检修提供参考依据。

基于模糊理论高职课程教学质量评价研究

课程思政是贯彻落实党的二十大精神入脑、入心、入行的有效渠道。为了引导高职课程思政建设的发展方向,提升教师的教学质量,解决融入什么内容、如何融入的问题,很有必要研究一套科学合理的课程思政教学质量评价体系。本文在习近平总书记二十大报告中提出的“全面贯彻党的教育方针,落实立德树人根本任务,培养德智体美劳全面发展的社会主义建设者和接班人”精神的指导下,通过系统分析形成了课程思政建设的两级评价指标,并利用模糊数学理论,探索建立了一套实践性强、科学有效的课程思政教学质量评价体系。

基于边缘计算的光通信网络安全风险等级评估方法

以提升光通信网络安全性为目的,提出基于边缘计算的光通信网络安全风险等级评估方法。首先构建光通信网络安全风险评估指标,并利用各边缘计算节点计算各终端指标数据。以各评估指标数据计算结果为基础,在云中心计算层内构建模糊综合评估模型,以模糊数学理论内的隶属度为基础,实现光通信网络安全风险多层次模糊评估。实验结果表明,该方法的光通信网络安全风险等级评估精度最高为97%,评估误差率最低为3%,该方法能够有效实现评估过程,基于评估结果进行网络优化能够提升光通信网络安全性能。

基于模糊MACBETH和生态位理论的PSS方案评价方法

针对多属性决策方法与生态位理论结合的PSS评价方法权重确定过程中一致性检验繁琐、权重计算过程复杂和未考虑评价语义的模糊性的问题,模糊MACBETH用于确定客户价值的权重和客户体验阶段提供客户价值的重要性系数;该方法无须复杂的计算过程,利用三角模糊数处理语义的模糊性且依托软件系统自动检验一致性,提高了评价效率和准确度。针对生态位优势仅适用于完全竞争状态的优势计算,将生态位重叠与生态位优势结合,提出的竞争优势公式可以体现方案间竞争程度和生态位优势表示方案间的竞争优势的优点,反映方案间真实的竞争状态。最后以某新能源汽车制造企业PSS方案评价问题为例验证了所提方法的有效性。

基于模糊理论的桥梁监测设备评估方法研究

针对桥梁监测设备运行状态难以量化评估的问题,提出一种基于模糊理论的综合评估方法,构建监测设备运行状态自诊断服务系统。基于现行规范建立监测设备运行状态评估指标体系,采用正态分布隶属度函数,并引入Pearson相关系数和信息熵确定各评估指标权重;采用基于混合算子的模糊评估模型,实现监测设备运行状态量化评估;通过自诊断服务系统,实时监测设备运行状态并进行报警。将该方法应用于某悬索桥监测设备评估,工程应用结果表明:该方法与规范方法和概率分析法评估结果相同,但该方法基于模糊理论“自下而上”逐级综合加权分析,全面量化评估桥梁监测设备体系各等级运行状态隶属度,评估结果简单直观,所构建的自诊断服务系统有助于科学指导桥梁健康监测系统运维。

基于改进STPA-DEMATEL的智能航电系统致因要素分析

针对智能航电系统在非线性耦合运行场景下产生的预期功能安全(safety of the intended functionality,SOTIF)问题,提出一种将系统理论过程分析(systematic theory process analysis,STPA)与决策试验与评价实验法(decision-making trial and evaluation laboratory,DEMATEL)相结合的致因分析框架。首先,在定义系统级危险的基础上构建安全控制结构,识别其不安全控制行为并提取与智能化缺陷相关的STPA致因要素。接下来,引入毕达哥拉斯模糊加权平均算子和闵可夫斯基距离对传统DEMATEL方法进行优化,专家根据控制反馈回路对致因要素进行评价并计算其中心度与原因度。最后,分析STPA致因要素与SOTIF致因属性之间的映射关系,给出关键致因要素的风险减缓措施。以单一飞行员驾驶(single-pilot operation,SPO)模式下的虚拟驾驶员助理系统为例说明了所提方法的可行性与有效性。研究结果表明,改进的STPA-DEMATEL方法可以有效识别关键致因要素,且能够克服专家评价的模糊性与不确定性,为智能航电系统的安全性设计提供了参考依据。

基于系统论的企业安全文化模糊层次(AHP-Fuzzy)综合评价

针对安全问题的特殊性,以系统论的思维方法,分析企业的安全系统(人-机-环境系统及其子系统),从整体上探讨安全文化的评价因素,建立了三大视角,8个要素,28条指标的企业安全文化评价体系。结合AHP方法和模糊(Fuzzy)综合评价理论,建立企业安全文化的AHP-Fuzzy综合评价模型,对企业安全文化建设进行科学的评价。结果表明,安全文化不仅体现在企业组织内部,还具有时间和空间属性,安全文化的评价指标应该从3个维度来测量。实证表明,该评价方法具有较强的适用性和可操作性,方便运用于各类型企业。

基于AHP-Fuzzy的装备保障指挥信息系统指挥控制效能评估

针对装备保障指挥信息系统的复杂性、随机性和模糊性特点,对其指挥控制效能的评估提出了层次分析法(AHP)和模糊理论(Fuzzy)相结合的综合评估方法;先对层次分析法和模糊理论进行阐述,而后建立了装备保障指挥信息系统指挥控制效能评估层次结构指标体系,并按照层次分析法相关理论,构建了装备保障指挥信息系统指挥控制效能评估模型,依据模糊理论结合实验数据对模型进行了求解,最后得出层次分析法和模糊理论相结合是评估装备保障指挥信息系统指挥控制效能有效方法的结论。