Approach to obtaining weights of uncertain ordered weighted geometric averaging operator

The ordered weighted geometric averaging（OWGA） operator is extended to accommodate uncertain conditions where all input arguments take the forms of interval numbers. First, a possibility degree formula for the comparison between interval numbers is introduced. It is proved that the introduced formula is equivalent to the existing formulae, and also some desired properties of the possibility degree is presented. Secondly, the uncertain OWGA operator is investigated in which the associated weighting parameters cannot be specified, but value ranges can be obtained and the associated aggregated values of an uncertain OWGA operator are known. A linear objective-programming model is established; by solving this model, the associated weights vector of an uncertain OWGA operator can be determined, and also the estimated aggregated values of the alternatives can be obtained. Then the alternatives can be ranked by the comparison of the estimated aggregated values using the possibility degree formula. Finally, a numerical example is given to show the feasibility and effectiveness of the developed method.

Generalized ordered weighted averaging operators based methods for MADM in intuitionistic fuzzy set setting

Multiattribute decision making（MADM） problems, in which the weights and ratings of alternatives are expressed with intuitionistic fuzzy（IF） sets, are investigated.Firstly, the relative degrees of membership and the relative degrees of non-membership are formulated as IF sets, the weights and values of alternatives on both qualitative and quantitative attributes may be expressed as IF sets in a unified way.Then a MADM method based on generalized ordered weighted averaging operators is proposed.The proposed method is illustrated with a numerical example.

Linear goal programming approach to obtaining the weights of intuitionistic fuzzy ordered weighted averaging operator

The multiple attribute decision making problems are studied, in which the information about attribute weights is partly known and the attribute values take the form of intuitionistic fuzzy numbers. The operational laws of intuitionistic fuzzy numbers are introduced, and the score function and accuracy function are presented to compare the intuitionistic fuzzy numbers. The intuitionistic fuzzy ordered weighted averaging （IFOWA） operator which is an extension of the well-known ordered weighted averaging （OWA） operator is investigated to aggregate the intuitionistic fuzzy information. In order to determine the weights of intuitionistic fuzzy ordered weighted averaging operator, a linear goal programming procedure is proposed for learning the weights from data. Finally, an example is illustrated to verify the effectiveness and practicability of the developed method.

Three methods for generating monotonic OWA operator weights with given orness level

Based on the properties of ordered weighted averaging (OWA) operator and regular increasing monotone (RIM) quantifier, three methods for generating monotonic OWA operator weights are proposed. They are geometric OWA operator weights, equidifferent OWA operator weights and the modified RIM quantifier OWA weights. Compared with most of the common OWA methods for generating weights, the methods proposed in this paper are more intuitive and efficient in computation. And as there are more than one solution in most cases, the decision maker can set some initial condition and chooses the appropriate solution in the real decision process, which increases the flexibility of decision making to some extent. All these three OWA methods for generating weights are illustrated by numerical examples.

Some OWA operator weights determination methods with RIM quantifier

Based on the quantifier guided method,an ordered weighted averaging(OWA)weights generating method under given orness level with regular increasing monotone(RIM)quantifiers is proposed.Then the RIM quantifier based OWA weights generating method is modified to make the generated weights be monotonic,which can be used to express the decision maker's consistent preference information.Finally,both of these weights generating methods are extended to their generic forms,so that they can generate the OWA weights for any ordinary elements set with any given aggregated value.

Interval-valued intuitionistic fuzzy aggregation operators

The notion of the interval-valued intuitionistic fuzzy set （IVIFS） is a generalization of that of the Atanassov＇s intuitionistic fuzzy set. The fundamental characteristic of IVIFS is that the values of its membership function and non-membership function are intervals rather than exact numbers. There are various averaging operators defined for IVlFSs. These operators are not monotone with respect to the total order of IVIFS, which is undesirable. This paper shows how such averaging operators can be represented by using additive generators of the product triangular norm, which simplifies and extends the existing constructions. Moreover, two new aggregation operators based on the t.ukasiewicz triangular norm are proposed, which are monotone with respect to the total order of IVIFS. Finally, an application of the interval-valued intuitionistic fuzzy weighted averaging operator is given to multiple criteria decision making.

A Self-determined Evaluation Method for Science Popularization Based on IOWA Operator and Particle Swarm Optimization

With the increase of science popularization, evaluation of science popularization has become an urgent demand. Considering science popularization bases as independent agents, a self-determined evaluation approach for science popularization using induced ordered weighted averaging (IOWA) operator and particle swarm optimization (PSO) is proposed in this paper.Firstly, six factors including science popularization personnel, space, fund,media, activity and influence are selected to construct an index system for science popularization evaluation. On this basis, the absolute dominance and relative dominance of evaluation indexes are used as induced components, and the prior order of the evaluation indexes is determined. Besides, the optimization model of index weighted vectors is established by IOWA operator, index weighted vectors are calculated by particle swarm optimization algorithm, and index weighted vectors and evaluation value vectors are obtain. Finally, the optimal evaluation vectors and evaluation results are given according to the Perron-Frobenius decision eigenvalve theorem .

基于博弈论组合赋权-云模型的消防员训练损伤风险评估

为降低消防员训练损伤发生率,从个人因素、环境因素和组织管理因素3个方面,提出包含19个指标的消防员训练损伤风险评估指标体系;采用Bonferroni有序加权平均算子(BON-OWA算子)修正层次分析法(AHP)的误差,获得各指标的主观权重;引用基于指标相关性的权重确定法(CRITIC)确定各指标的客观权重;基于博弈论组合赋权法确定各指标的组合权重;引入云模型理论,建立消防员训练损伤风险评估模型,并以河南省某消防救援大队消防员的训练为例,验证该模型的科学性。结果表明:训练装备的战技性能状况指标对消防员的训练损伤风险等级影响最大;该风险评估模型通过数据处理,可以确定消防员训练损伤风险等级;案例中,消防员在5种不同情景下的训练损伤风险等级均为一般风险,这与模糊综合评价法所得结果一致。

基于IOWA算子的空气质量组合预测

对空气质量指数进行预测,反映数据波动的趋势与规律,提出基于诱导有序加权平均(IOWA)算子的Prophet-EEMD-XGBoost模型。首先利用Prophet算法将数据分解为趋势项、季节项、节假日、误差项;然后集合经验模态分解EEMD将原始数据分为9项IMF以及残余分量。最后,利用XGBoost模型对各分量分别进行预测,预测结果利用MAE、MAPE、RMSE、RMSPE进行评价。结果表明,相较于一些单预测模型与其他组合预测模型,基于IOWA算子的Prophet-EEMD-XGBoost组合预测模型有更好的预测效果。

基于OWA算子的模糊n-cell数排序方法

针对基于模糊n-cell数的多属性排序问题,提出了一种基于有序加权平均算子(OWA算子)的模糊n-cell数排序方法。该方法首先根据样本数据对评估对象的属性构造模糊n-cell数,其次根据均值将属性按照从大到小排列,然后选取合适的权重向量,应用OWA算子进行信息聚合得到综合模糊n-cell数,接着根据各分量均值得到排序结果。最后,将该方法运用到实例中,并与传统的均值方法进行了比较。结果表明该方法不仅灵活有效,可根据具体情况选择不同的OWA权重来消除部分不合理的情况,使结果更有说服力,还弥补了传统均值方法的不足。

改进综合因子法的数控伺服刀架可靠性分配方法

针对可靠性分配过程中影响因素和专家打分的不确定性问题,提出一种将灰色关联理论、模糊集理论和有序加权平均算子结合的基于改进综合因子法的数控伺服刀架可靠性分配方法。综合考虑技术水平、重要度、复杂度、运行环境、维修性、费效比等多种影响因素,将各因素细化并分层,建立分层的综合因子评价模型,并给出评价标准。综合现场试验得到的统计数据,建立子系统的全局指标模型,得到每个子系统的全局指标,以故障率为分配指标将数控刀架系统的总体故障率分配到各子系统,实现数控伺服刀架的可靠性分配,得到的可靠度分配结果为驱动系统2.193×10-5、传动装置1.645×10-5、锁紧松开及定位装置2.547×10-5、刀盘信号装置盘1.345×10-5、密封与紧固装置4.589×10-5和连接线与连接界面4.544×10-5,与数控伺服刀架的工程实际情况相符。

基于IOWA算子的航班订座组合预测模型

航班订座预测的经典方法包括回归模型、增量模型、指数平滑模型等,在复杂多变的非线性数据环境下,各单项模型存在预测效果不稳定及健壮性不满意的情况。为解决这一问题,本文构造了一个基于诱导有序加权平均(IOWA,induced ordered weighted averaging)算子的航班订座组合预测模型,组合模型采用各单项模型的预测精度作为诱导因子,动态计算单项预测模型组合权重及构建未来的动态预测组合。但传统的IOWA组合预测模型对随机波动较大的数据适应性较差,本文在深入分析航班旅客订座量数据规律的基础上,改进了该模型在预测期的诱导因子,取得了较好的预测结果。

改进二维云模型的信息工程监理风险评估

针对传统信息工程监理风险评估具有随机性与模糊性,提出了改进二维云模型的风险评估模型。通过三角模糊层次分析理论确定各风险因素权重,以可能性和严重度为基础变量,构建基于二维云模型的风险云图,在加权有序几何平均算子(order weighted geometric averaging operator,OWGA)的基础上引入情境参数,根据决策者的风险偏好优化各准则层风险云与云标尺的贴近度排序。经过实例仿真,表明所提模型可以在融合专家智慧的同时,避免定性云转换为定量值的损失,将风险以蕴含随机性和模糊性的形式可视化,经改进贴近度确定风险等级,验证了其有效性及优越性。

An anomaly detection method for spacecraft solar arrays based on the ILS-SVM model

Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions.When a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnormal damage occurs.This damage makes solar array power generation unable to fully meet the energy demand of a spacecraft.Therefore,timely and accurate detection of solar array anomalies is of great significance for the on-orbit operation and maintenance management of spacecraft.In this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine(ILS-SVM)model:it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine(LS-SVM)submodels by training on these training subsets,respectively;after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on;finally,based on the obtained ILS-SVM model,a parameterfree and unsupervised anomaly determination method is proposed to detect the health status of solar arrays.We use the telemetry data set from a satellite in orbit to carry out experimental verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.

基于改进SLIM方法的管制移交人误概率预测

基于模糊有序加权平均算子(Fuzzy Ordered Weighted Averaging Operator,FOWA)对成功似然指数法(Success Likelihood Index Methodology,SLIM)进行了改进,实现了对管制移交人误概率实施定量评估的目的。相比SLIM基本方法,改进方法可使评估过程更符合人的模糊判断特性,还能显著提升评估效率,在数据处理过程中及时过滤不良的判断信息。通过实际应用中的对比分析可知,虽然两种方法均能得出管制移交人误概率,但改进SLIM方法对专家知识的挖掘更充分。使用Delphi法对管制移交过程中的人误行为触发概率可能性进行排序,排序结果与改进SLIM方法一致度更高,由此验证了改进SLIM方法的优势与可行性。

基于FGM-SVR组合模型的港口吞吐量预测

为提高港口吞吐量的预测精度,建立基于分数阶累加GM(1,1)预测模型FGM(1,1)和支持向量回归(support vector regression, SVR)的组合预测模型进行港口吞吐量的预测。首先,分别运用FGM(1,1)模型和SVR模型对吞吐量进行预测;然后,针对传统组合模型赋权不能兼顾各单项模型在各时点预测能力强弱的问题,提出基于诱导有序加权平均(induced ordered weighted averaging, IOWA)算子的赋权方法进行组合预测;最后,以重庆港2005—2020年港口货物吞吐量为数据样本进行实例验证,分别使用FGM(1,1)模型、SVR模型和赋权后的组合模型进行港口吞吐量预测,并比较3种模型的预测精度;最后,分别使用这3种模型对2021—2024年港口吞吐量进行了预测。研究结果表明:基于IOWA组合预测模型的均方根误差、平均绝对误差和平均绝对百分比误差均明显小于各单一预测模型。该组合模型可以为港口吞吐量预测提供一种新的方法。

混合信息融合策略下共享电单车绿色回收供应商选择方法

共享电单车具有的速度快、舒适度高等特点使其拥有大量用户,运营商以过度投放策略来抢占市场先机进而引发了电单车故障率高、停放难且不规范等现实问题,增大了回收管理的难度。为此,研究了一种共享电单车绿色回收供应商选择方法,为回收管理提供决策支撑。首先,分析了共享电单车的回收需求特征,构建供应商评价指标体系,并引入区间直觉模糊集提出混合评价信息统一量化方法;其次,为集结群体评价信息,提出内嵌双参数的连续区间直觉模糊有序加权平均算子,并以群体一致性为目标构建了参数求解模型;然后,以所提算子为基础,提出用于解决回收供应商选择问题的群体决策方法;最后,通过共享电单车回收案例分析验证了所提方法的适用性与有效性。

基于目标规划的语言值OWA算子及其在大学生综合测评的应用

针对大学生综合测评问题,设计一种基于高斯模糊集的有序加权平均(OWA)评价方法,方便评价者借助高斯模糊集对评价对象的各指标给出语言值评价.定义了高斯模糊集的数乘运算和排序方法,以评价者的乐观水平为参数,设计一种基于目标规划的权重优化方法,引导大学生全面发展或个性化发展.实验结果表明,设置合理的乐观水平,所提出的评价模型可以鼓励学生发挥自己的特长,得到与主观感觉一致的评价结果.

基于IOWC-GOWA算子的区间组合预测模型

在连续区间数广义有序加权平均(C-GOWA)算子基础上,将IOWA算子和C-GOWA算子相结合,提出诱导有序加权连续区间广义有序加权平均(IOWC-GOWA)算子的概念。以指数支撑度作为相关性指标,构建新的组合预测模型。通过实例分析说明该模型能有效提高预测精度。

基于诱导有序加权平均算子和马尔可夫链的中长期电力负荷组合预测模型

针对传统中长期电力负荷组合预测方法的缺陷,将诱导有序加权平均算子(induced ordered weighted averaging,IOWA)与马尔可夫链(Markov chain,MC)相结合构建IOWA-MC组合预测模型。该模型根据每个单项预测方法在各时点拟合精度的高低顺序对其赋权,保证了组合预测模型中权系数与拟合精度在任一时点上的相关性,同时利用MC定性推测出预测时间点上各单项预测方法的预测精度状态,从而确定其在预测时点上的权系数。算例结果表明,IOWA-MC能自动识别高精度预测模型,排除低精度预测方法带来的影响,预测精度较高,具有较强的实用价值。