A Multi-Attribute Decision-Making Method Using Belief-Based Probabilistic Linguistic Term Sets and Its Application in Emergency Decision-Making

Probabilistic linguistic term sets(PLTSs)are an effective tool for expressing subjective human cognition that offer advantages in the field ofmulti-attribute decision-making(MADM).However,studies have found that PLTSs have lost their ability to accurately capture the views of decision-makers(DMs)in certain circumstances,such as when the DM hesitates between multiple linguistic terms or the decision information is incomplete,thus affecting their role in the decision-making process.Belief function theory is a leading streamof thought in uncertainty processing that is suitable for dealing with the limitations of PLTS.Therefore,the purpose of this study is to extend PLTS to incorporate belief function theory.First,we provide the basic concepts of the extended PLTS(i.e.,belief-based PLTS)through case analyses.Second,the aggregation operator of belief-based PLTS is defined with the ordered weighted average(OWA)-based soft likelihood function,which is improved by considering the reliability of the information source.Third,to measure the magnitude of different belief-based PLTSs,the belief interval of singleton is calculated,and the comparison method of belief-based PLTS is constructed based on probabilities.On the basis of the preceding discussion,we further develop an emergency decision framework that includes several novel techniques,such as attribute weight determination and decision information aggregation.Finally,the usefulness of the framework is demonstrated through a case study,and its effectiveness is illustrated through a series of comparisons.

Damage effectiveness assessment method for anti-ship missiles based on double hierarchy linguistic term sets and evidence theory

The research on the damage effectiveness assessment of anti-ship missiles involves system science and weapon science, and has essential strategic research significance. With comprehensive analysis of the specific process of the damage assessment process of anti-missile against ships, a synthetic damage effectiveness assessment process is proposed based on the double hierarchy linguistic term set and the evidence theory. In order to improve the accuracy of the expert ’s assessment information, double hierarchy linguistic terms are used to describe the assessment opinions of experts. In order to avoid the loss of experts ’ original information caused by information fusion rules, the evidence theory is used to fuse the assessment information of various experts on each case. Good stability of the assessment process can be reflected through sensitivity analysis, and the fluctuation of a certain parameter does not have an excessive influence on the assessment results. The assessment process is accurate enough to be reflected through comparative analysis and it has a good advantage in damage effectiveness assessment.

Long-term Traffic Volume Prediction Based on K-means Gaussian Interval Type-2 Fuzzy Sets

This paper uses Gaussian interval type-2 fuzzy se theory on historical traffic volume data processing to obtain a 24-hour prediction of traffic volume with high precision. A K-means clustering method is used in this paper to get 5 minutes traffic volume variation as input data for the Gaussian interval type-2 fuzzy sets which can reflect the distribution of historical traffic volume in one statistical period. Moreover, the cluster with the largest collection of data obtained by K-means clustering method is calculated to get the key parameters of type-2 fuzzy sets, mean and standard deviation of the Gaussian membership function.Using the range of data as the input of Gaussian interval type-2 fuzzy sets leads to the range of traffic volume forecasting output with the ability of describing the possible range of the traffic volume as well as the traffic volume prediction data with high accuracy. The simulation results show that the average relative error is reduced to 8% based on the combined K-means Gaussian interval type-2 fuzzy sets forecasting method. The fluctuation range in terms of an upper and a lower forecasting traffic volume completely envelopes the actual traffic volume and reproduces the fluctuation range of traffic flow.

基于中医药学语言系统的中医药优势病种术语集构建研究

目的探讨基于顶层标准结合底层数据的方法构建中医药优势病种术语集。方法整理课题前期研究抽取的文献数据,利用中医术语分析工具,基于前期构建的中医药优势病种文献语义网络模型框架,将提取的数据按不同类型与中医药学语言系统(TCMLS)中相应的语义类型概念进行匹配映射,匹配结果进一步人工规范。结果纳入11个中医药优势病种文献,将抽取的数据利用术语分析工具,与TCMLS中的对应类型概念进行匹配映射,结合人工规范,形成中医药优势病种术语集。结论本套术语集既符合中医药优势病种文献特征,又符合信息技术特征,可为实现中医药优势病种文献数据概念的结构化表达和计算机深度利用提供基础,顶层标准与底层数据相结合的方法可为中医药领域术语集的构建提供思路。

基于多粒度犹豫模糊语言术语集的TOPSIS决策方法研究

为了解决在实际决策时,由于知识背景不同决策者采用不同粒度语言术语集来表达而导致决策结果不准确的问题,本文提出了一种基于多粒度犹豫模糊语言术语集的逼近理想解排序(technique for order preference by similarity to ideal solution,TOPSIS)决策方法。首先选用各术语集中的最大粒度作为标准粒度,通过转换算法将每个决策者的语言术语集转换到同一标准粒度下进行集结,得出相应的隶属度语言术语集;然后结合TOPSIS方法,计算每个备选方案与正、负理想点距离,以相对贴近度的大小排序实现最优方案的选择;最后,通过一个实例,验证该方法的可行性和优越性。本文所提方法可应用于最优方案的选择问题中,提升决策结果准确度。

基于FDHHFLTS-BN的海底管道泄漏失效风险定量分析

为预防海底油气管道泄漏失效事故,提出基于自由双层次犹豫模糊语言术语集(FDHHFLTS)和贝叶斯网络(BN)的FDHHFLTS-BN风险分析方法,用于分析海底油气管道泄漏失效事故概率及事故的关键风险因素。将故障树模型转换为BN结构,由专家根据FDHHFLTS评估基本事件发生可能性;采用最佳最差法(BWM)确定专家权重,结合相似性聚合方法(SAM)聚合专家意见;依据构建的BN模型,正向推理得到事故发生概率,反向推理得到后验概率,并进行敏感性分析。将该方法应用于实例分析,结果表明:分析段海底管道泄漏事故的概率值为P=6.20×10^(-3);焊缝施工缺陷、材料施工缺陷和渔具作用等为事故发生的关键因素;与传统方法对比分析结果证明,所提方法在确定海底管道风险方面具有一定的优势。

基于调和概率语言术语集的Power平均算子

实践发现现存概率语言Power平均算子在应用中存在概率信息丢失、计算过于繁琐等问题,针对这些问题,提出一种基于调和概率语言术语集的Power平均算子。结合新的基本运算法则与新的支撑度,给出新的概率语言Power平均算子、概率语言Power几何平均算子、概率语言Power加权平均算子、概率语言Power加权几何平均算子,同时论证分析了这几类新型算子的性质。最后通过实验数据,将提出的算子与现存算子进行对比分析,验证了所提算子的有效性与可行性。

基于犹豫模糊混合云模型的航空装备保障系统效能评估

针对航空装备保障系统效能评估过程存在定性指标与定量指标共存、评价信息具有不确定性等问题,提出了一种基于犹豫模糊混合云模型的效能评估方法。根据语言变量的转换与正态云模型之间的混合规则,将犹豫模糊语言术语集转换为梯形云模型,并根据云模型的数字特征,提出了犹豫模糊语言术语集的犹豫度函数与标度函数,实现了对定性指标的量化,兼顾了评价信息的犹豫性、模糊性和随机性。利用区间层次分析法结合灰狼优化算法对效能评估指标体系进行权重赋值,确定系统效能等级。通过应用实例与方法比较表明:犹豫模糊混合云模型不仅评估结果可靠,而且能够识别系统的运行状态,发现系统建设的短板,为提高系统效能提供重要的参考意见,更具有实用性。

考虑期望强度与属性补偿关系的新能源汽车在线推荐方法

基于消费者属性期望,结合柔性决策和补偿原则理论,定义期望强度与属性补偿关系,明确属性期望与期望强度融合方法以及产品属性补偿规则,提出考虑期望强度和属性补偿关系的新能源汽车在线筛选方法。结合证据理论与消费者风险偏好提出新能源汽车在线排序方法,实现在线推荐满足消费者期望的新能源汽车。研究发现:与考虑属性期望严格约束的推荐方法相比,同时考虑期望强度与属性补偿关系能够避免综合高性价比车型因部分消费者属性期望严格约束而被忽略;采用证据推理结合消费者风险偏好确定车型优先级,能够更加精准地为消费者推荐满足个性化期望的新能源汽车。

An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods

Hydrogen is the new age alternative energy source to combat energy demand and climate change.Storage of hydrogen is vital for a nation’s growth.Works of literature provide different methods for storing the produced hydrogen,and the rational selection of a viable method is crucial for promoting sustainability and green practices.Typically,hydrogen storage is associated with diverse sustainable and circular economy(SCE)criteria.As a result,the authors consider the situation a multi-criteria decision-making(MCDM)problem.Studies infer that previous models for hydrogen storage method(HSM)selection(i)do not consider preferences in the natural language form;(ii)weights of experts are not methodically determined;(iii)hesitation of experts during criteria weight assessment is not effectively explored;and(iv)three-stage solution of a suitable selection of HSM is unexplored.Driven by these gaps,in this paper,authors put forward a new integrated framework,which considers double hierarchy linguistic information for rating,criteria importance through inter-criteria correlation(CRITIC)for expert weight calculation,evidence-based Bayesian method for criteria weight estimation,and combined compromise solution(CoCoSo)for ranking HSMs.The applicability of the developed framework is testified by using a case example of HSM selection in India.Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.

基于变分模态分解的卷积长短时记忆网络短期电力负荷预测方法

电力负荷序列易受多重外部因素影响而呈现复杂性,不利于精准预测。为此,提出一种基于变分模态分解(variational mode decomposition,VMD)的卷积神经网络和长短期记忆网络(convolutional neural network and long short-term memory network,CNN-LSTM)相结合的短期电力负荷并行预测方法。先采用VMD将负荷数据分解为规律性强的各本征模态函数(intrinsic mode function,IMF)及残差;再将各分量分别输入到各自对应的CNN-LSTM混合预测网络,获得各初始预测值,并将该值与由气候、日期类型等组合得到的相关因素特征集相结合,进一步得出修正预测值;最终,叠加各分量修正预测值即得到完整预测结果。在实际负荷数据上做验证分析,结果表明,考虑相关外部因素特征集后日负荷预测平均相对误差均值可降低2.18%。与几种常规负荷预测方法进行效果对比,验证了该方法的有效性和可行性。

社会网络下基于在线评论的旅游供应商优选研究

在线旅游平台上评论规模庞大,信息量浩如烟海,平台如何依据他人评论信息为游客提供最优旅游供应商,从而使游客得到满意的服务体验是亟待解决的问题。在社会网络视角下,基于在线评论和概率语言提出一种旅游供应商的优选方法。首先,采集旅游平台上的游客评论数据,运用TF-IDF方法提取各供应商相关评论的关键词,通过筛选整理形成主题并计算其权重;然后,分析每条评论的情感值,并将其转换成概率语言术语集的形式;最后,构建游客群体社会网络,将游客群体意见与在线评论信息相结合,运用PROMETHEE-Ⅱ方法对旅游供应商进行优选排序。结果表明,本文方法推荐结果与旅游平台数据基本一致,但能在相似评分的供应商中做出优劣比较,精准有效地提供最优供应商,且简单可靠。

基于SSA-Hurst-ARIMA组合模型的船舶柴油发电机组故障特征短期预测

为提高船舶柴油发电机组故障特征短期预测精度,建立基于奇异谱分析(Singular Spectrum Analysis,SSA)、Hurst指数、自回归移动平均(Auto-Regressive Integrated Moving Average,ARIMA)的组合预测模型。以某试验中船舶柴油发电机组运行数据为基础,选取增压器滑油压强数据,对比分析单一ARIMA模型、SSA主成分-ARIMA组合模型和SSA-Hurst-ARIMA组合模型的预测效果。结果表明,SSA-Hurst-ARIMA组合模型的预测效果优于单一ARIMA模型和SSA主成分-ARIMA组合模型,更适合应用于船舶柴油发电机组故障特征的短期预测。

基于EMD-LSTM模型的水轮机组实测摆度信号预测方法研究

水电机组的运行状态直接影响电站及电网的安全稳定,预测机组监测的振动信号有助于改善故障诊断的缺陷。为此,将经验模态分解(EMD)和神经网络模型相结合,提出一种基于EMD-LSTM的水轮机组摆度信号预测模型,将该模型应用于国内某水电站的机组摆度信号预测中,并与LSTM、GA-BP和EMD-GABP模型预测结果进行比较。结果表明,该模型在机组摆度信号的预测方面表现出较高的精度,且优于其他模型。

基于时空注意机制与LSTM的暂态电压稳定评估

针对新型电力系统发展背景下现阶段机器学习算法难以准确判断风电并网系统暂态电压稳定性的问题,提出一种基于时空注意机制与长短期记忆网络的暂态电压稳定评估方法。首先,基于支持向量机对初始单特征评价进行特征粗筛,并采用皮尔逊相关系数法判断剩余特征的相似度;其次,通过主成分分析-加权负荷评价获取与暂态电压稳定情况相关性较高的特征集;然后,通过时空注意机制,量化系统负载节点间的空间耦合关系和风电接入点间的空间相关性对整个系统暂态进程的影响,构建基于时空注意机制与长短期记忆网络的评估模型;最后,在算例上进行仿真分析,结果表明该模型有利于风电并网系统暂态电压稳定性判别准确率的提升,以及减少误判和漏判。

关于环评报告中大气环境防护距离设置的研究

2018年12月1日,2018年版大气环境导则实施,其对大气环境防护距离的定义与2008年版定义并不一致,时至今日,部分环保管理工作人员对此并未注意到,导致仍存在大量的认知偏差,甚至争议。正是基于此,根据大气环境导则中的相关规定,结合环评实际工作经验,对大气环境防护距离设置进行了研究分析,以供广大环境影响评估工作人员参考。

什么样的长效机制政策组合促进房地产市场平稳健康发展——基于NCA和fsQCA的实证分析

什么样的政策组合促进房地产市场平稳健康发展是房地产长效机制研究的重要问题。使用NCA和fsQCA方法,从中央和地方两个层级分析了房地产长效机制的政策组态。研究发现:(1)房地产长效机制的单一政策与工具,不构成“稳房价、稳地价、稳预期”目标的必要条件;(2)中央层级的政策组合重在产业主导下的稳定逻辑,旨在为房地产业平稳健康发展塑造稳定的产业发展环境;其中,4种政策组合可以实现房价稳定,包括分别依托收入分配与立法的考核目标与货币政策驱动型、立法驱动型、财政货币政策驱动型;3种政策组合可以实现地价稳定,包括依托立法的财政货币政策驱动型、改革逻辑下的考核与收入驱动型、依托收入分配的财政驱动型;(3)地方层级的政策组合重在结构主导逻辑,所有政策组态表现出明显的结构政策主导的逻辑特征;其中,4种政策组合可以实现房价稳定,均为财税土地驱动型,分别依托住房制度、信贷政策、住房制度和信贷政策、宏观审慎政策;5种政策组合可以实现地价稳定,包括依托宏观审慎的财税驱动型、依托土地供给的信贷财税驱动型、依托财税信贷的住房制度驱动型、依托财税信贷和宏观审慎的住房税收驱动型、依托财税信贷和土地供给的住房税收驱动型。研究为我国中央和地方政府进一步完善房地产长效机制,提升政策组合效率,促进房地产市场平稳健康发展提供了依据。

Fuzzy interval linguistic sets with applications in multi-attribute group decision making

Uncertain and hesitant information, widely existing in the real-world qualitative decision making problems, brings great challenges to decision makers. Hesitant fuzzy linguistic term sets(HFLTSs), an effective linguistic computational tool in modeling and eliciting such information, have hence aroused many scholars’ interests and some extensions have been introduced recently.However, these methods are based on the discrete linguistic term framework with the limited expression domain, which actually depict qualitative information using several single values. Therefore,it is hard to ensure the integrity of the semantics representation and the accuracy of the computation results. To deal with this problem, a semantics basis framework called complete linguistic term set(CLTS) is designed, which adopts a separation structure of linguistic scale and expression domain, enriching semantics representation of decision makers. On this basis the concept of fuzzy interval linguistic sets(FILSs) is put forward that employs the interval linguistic term with probability to increase the flexibility of eliciting and representing uncertain and hesitant qualitative information. For practical applications, a fuzzy interval linguistic technique for order preference by similarity to ideal solution(FILTOPSIS) method is developed to deal with multi-attribute group decision making(MAGDM) problems. Through the cases of movie and enterprise resource planning(ERP) system selection, the effectiveness and validity of the proposed method are illustrated.

基于改进EVM的雷达PRI调制类型开集识别

雷达脉冲重复间隔(PRI)的调制类型是分析雷达工作状态和任务的重要手段。针对常见PRI调制类型识别算法无法识别未知调制类型的问题,文中提出一种基于改进极值机(EVM)的雷达PRI调制类型开集识别方法。首先,采用残差-双向长短时记忆网络进行PRI序列的特征提取;其次,结合原型学习,利用基于距离的交叉熵损失和原型损失对特征提取网络进行训练;最后,在特征空间中引入已知类特征的线性组合以模仿未知类的行为,提出了改进的EVM模型。实验结果表明,与EVM相比,文中所提方法能够提升雷达PRI调制类型的识别准确率,且在开放的电磁环境下具有良好的开集适应性。

基于VMD-ISSA-LSTM的短时交通流预测研究

针对城市短时交通流随机波动性强、可靠性低、预测精度差等问题,将变分模态分解(VariationalMode Decomposition,VMD)和改进麻雀搜索算法(ImproveSparrowSearchAlgorithm,ISSA)与长短期记忆(LongShort-Term Memory, LSTM)神经网络相结合,建立一种短时交通流预测模型(VMD-ISSA-LSTM)。首先利用VMD对历史原始交通流数据进行分解;然后采用佳点集、正弦函数扰动和Tent混沌映射等策略对标准的SSA算法加以改进,增强ISSA算法的寻优能力;最后,将每个分量送入ISSA-LSTM中进行预测,同时将预测结果线性叠加,得到交通流量预测值。以上海市中山北路-曹杨路口2018年11月1日—30日的历史交通数据对模型进行验证。结果表明,与LSTM、VMD-LSTM、VMD-SSA-LSTM等传统预测模型相比,VMD-ISSA-LSTM模型的预测结果的平均绝对百分比误差为1.278 4%,能够更好地应用于短时交通流预测中。