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.

基于软判决反馈的猝发通信同步算法

针对无线猝发通信中存在的帧同步与符号定时同步问题,提出了一种适用于衰落信道的基于软判决反馈的同步算法。算法利用滑动时间窗采样得到接收符号序列,其中同步头用来估计衰落和信噪比,重点研究利用未知PSK或QAM数据符号进行软判决来构建新的似然函数,依此进行似然函数计算,当似然函数值超过同步门限时完成信号帧同步,并确定搜索区间进行似然函数峰值搜索,最后对峰值及相邻两个值进行高斯插值以获取更精确的结果。仿真结果表明:在同步头长度为16,帧长度为80时,该算法在未知数据符号为PSK符号时,算法的MSE性能在低信噪比条件下即可超过长度为16的克拉美罗界理论线(CRB(16)),并在高信噪比条件下,逼近CRB(80);在未知数据为QAM调制时,算法性能在低信噪比与高信噪比条件下均优于CRB(16)。

基于软似然函数的直觉模糊多属性决策方法

为解决传统似然函数中结果值受极端数据影响及无法全面反映决策者偏好程度等问题,本文提出基于软似然函数的直觉模糊多属性决策方法。首先,针对具有不确定性的多属性决策信息,利用直觉模糊集表示模糊信息,对不确定的属性信息进行统一描述,构建了一个直觉模糊环境下的多属性决策问题。其次,通过引入态度特征识别决策者的主观偏好,考虑幂有序加权平均算子下各属性间的支持度,从而确定属性权重,在此基础上采用基于软似然函数的多属性决策算法对多个概率证据进行有效聚合。实验结果表明,所得软似然函数值更趋近于线性变化,不仅能更好地软化加权值,反映决策者的偏好信息,而且能在一定程度上减弱极端数据对结果产生的影响。

多属性决策方法在电诈犯罪嫌疑人认定中的应用

为协助警方在侦破电信网络诈骗案件时实现对所获证据的有效聚合,得到多名涉案人员的嫌疑程度排序,提出了一种基于语言毕达哥拉斯模糊集的多属性决策方法。首先,根据已有线索确定相关涉案人员及属性信息;其次,基于语言毕达哥拉斯模糊集以定性的语言术语表示模糊信息,对不确定的属性信息进行统一描述;最后,在语言毕达哥拉斯模糊环境下,利用基于WOWA算子的软似然函数这一多属性决策方法对各属性值进行有效聚合,得到相关涉案人员综合得分值的结果排序。结果表明,相较于定量的评估形式,定性的语言术语评估形式更适用于实际电诈犯罪案件;同时实验结果表明,相较于基于OWA算子的软似然函数多属性决策方法,本文所采用的WOWA算子能在一定程度上避免极端数据带来的影响,并且能更好地软化权重值,使得软似然值的变化更趋于平缓。本文所提多属性决策方法可协助警方关联与案件有关的属性信息,在弱化决策者主观因素的前提下缩小人工甄别范围以获得涉案人员的嫌疑程度结果排序,本文首次将该技术应用于电信网络诈骗案例中,对电诈犯罪嫌疑人认定具有实际意义。