基于证据理论的多传感器加权融合

多传感器数据融合多基于证据理论进行加权,但识别框架确定和基本可信度分配是关键.本文简述了证据理论并分析了总均方误差最小意义下加权融合的缺点,在引入抽象传感器的基础上,根据M函数的区间覆盖次数进行基本可信度分配,确定对历史数据和各个传感器的支持度,然后进行加权融合.仿真表明,这种基于证据理论的多传感器加权融合方法只需要很少的先验信息,但具有较好的融合效果和鲁棒性.     

多传感器信息融合利器——D-S证据理论初探

多传感器信息融合广泛应用于自动目标识别、战场监视、机器人、工业过程控制、遥感、图象处理、模式识别等领域。采用信任函数作为度量的证据理论可处理由不知道所引起的不确定性。本文对信息融合技术的概念以及重要融合方法进行初探。     

用D-S证据理论实现多传感器信息融合的一种改进算法

阐述了基于D-S证据理论的多传感器信息融合算法,提供一种基于D-S理论的改进方法以解决融合信息的相关性问题.用滑觉和热觉传感器作实验,对该方法的有效性进行了验证.     

基于证据理论的多传感器加权融合改进方法

针对多传感器系统中高冲突证据合成问题,考虑到传感器同时具有固有可靠性与实时可靠性的特点,基于证据理论提出了一种新的多传感器加权融合方法;该方法通过计算证据间的相似矩阵获取证据的后验权重,并结合根据传感器固有可靠性预先分配的先验权重,得到证据体的复合权重,然后据此对原始证据进行加权平均,最后利用D-S证据组合规则合成加权平均后的证据;实例仿真表明,与D-S、Yager、Murphy等方法相比,该方法能够更好地处理高冲突证据,且收敛速度更快。     

基于D2S 证据理论的移动机器人 多传感器信息融合方法研究与应用

针对全自主式移动机器人，以首届CCTV全国机器人电视大赛为背景，提出了通过D-S证据理论和模糊集合理论相结合的方法，对多传感器信息进行融合，较好地解决了机器人在复杂环境下运动的多传感器信息融合问题，实现了移动机器人的准确定位。     

基于效用理论的多传感器智能融合

信息融合系统应综合考虑获取信息所要求的资源、计算复杂度和时间所需要的最小成本,提出了基于效用理论的传感器智能融合技术,利用期望效用最大化的观点来选择传感器,并与证据理论相组合,应用于目标识别,仿真结果表明该方法的有效性。     

基于证据理论的多分类器融合方法研究

证据理论是建立在独立性假设基础上,理论和实际应用都需要突破这一限制。最近提出的一种相关证据模型认为,两个相关证据由一个相关源证据分别与两个独立源证据通过正交和合成得到,相关证据的合成可以归结为这三源证据的正交和,为此首先要由相关证据和相关源证据辩识独立源证据,这是证据理论中的反问题,其解是否有意义取决于相关源证据是否合适。该文给出一个充分条件,如果相关源证据满足此条件,反问题有唯一有意义的解,在此指导下,研究了字符识别中的多分类器融合问题,实验结果表明,识别性能优于传统证据理论方法。     

基于统计证据的mass函数和D-S证据理论的多传感器目标识别

mass函数表示对证据的精确信任程度,是信任函数的基本概率分配.文章在阐述Dempster-Shafer(D-S)证据理论和决策方法的基础上,较系统地论述了基于统计证据的mass函数和D-S证据理论的目标识别的数据融合方法,并给出了具体的识别实例.从计算结果可以看出,该方法有利于目标识别的实现,具有较好的实用性.     

一种D-S证据理论的多传感器数据融合算法

针对多传感器网络中采集的数据存在的不确定性,提出了一种基于D-S证据理论的多传感器数据融合算法.该算法分同类数据融合和异类数据融合两步,首先对多传感器得到的数据取特征值,通过计算同种数据间的距离,得到信任函数并设置阈值剔除异常值,将得到的正常同类数据进行初步融合.其次,计算异类数据与各等级特征值间的距离,对得到的距离求...     

基于加权D-S证据理论的分布式多传感器目标识别

针对分布式多传感器环境下的目标识别问题,提出了一种基于加权D-S证据理论组合规则的决策融合方法。分析了多传感器目标识别系统的信息模型,指出传感器的决策可信度由其被支持度及与目标间的距离确定。将该可信度体现为加权D-S证据理论组合规则中的证据权值,综合考虑传感器支持度及其与目标距离,给出了权值确定方法。仿真实验证明方法提高了融合效率,可较快完成识别任务。     

Evidential Reasoning Approach for Multiattribute Decision Analysis Under Both Fuzzy and Interval Uncertainty

Many multiple attribute decision analysis (MADA) problems are characterized by both quantitative and qualitative attributes with various types of uncertainties. Incompleteness (or ignorance) and vagueness (or fuzziness) are among the most common uncertainties in decision analysis. The evidential reasoning (ER) and the interval grade ER (IER) approaches have been developed in recent years to support the solution of MADA problems with interval uncertainties and local ignorance in decision analysis. In this paper, the ER approach is enhanced to deal with both interval uncertainty and fuzzy beliefs in assessing alternatives on an attribute. In this newly developed fuzzy IER (FIER) approach, local ignorance and grade fuzziness are modeled under the integrated framework of a distributed fuzzy belief structure, leading to a fuzzy belief decision matrix. A numerical example is provided to illustrate the detailed implementation process of the FIER approach and its validity and applicability.      

不确定性多属性决策中的ER方法改进

基于对不确定性多属性决策问题中ER方法的研究,提出一种不确定性多属性决策中的改进ER方法,井证明该方法完全满足证据合成的4个公理．通过实例运算,进一步验证了新方法的有效性和合理性．     

Induced Ordered Weighted Evidential Reasoning Approach for Multiple Attribute Decision Analysis with Uncertainty

We are primarily concerned with the problem of aggregating multiple attributes with uncertainty to form an overall decision function. We introduce a new type of approach for aggregation called an induced ordered weighted evidential reasoning (IOWER) approach, which is inspired by an induced ordered weighted averaging operator and the evidential reasoning (ER) approach. In the IOWER approach, we use a belief decision matrix combined with an induced ordered weighting vector for problem modeling and the Dempster–Shafer theory of evidence for attribute aggregation. It is proved that the original ER algorithm is a special case of the IOWER algorithm. Then we examine the properties of the IOWER approach. One key point in the IOWER approach is to reorder the arguments in the form of distributed assessment structure. A kind “expected utility” order‐inducing variable is proposed in the IOWER approach, which can make the alternative's advantages prominent. Finally, we present an illustrative example in which the result obtained with the new aggregation approach can be seen.     

一种新的不确定推理方法

提出了一种基于认知结构的不确定推理方法:采用四值认知结构表达不确定知识,采用定义在认知结构上的双向认知推理结构来处理推理规则的不确定性.介绍的不确定推理方法可以包容精确的概率推理、容忍信息的不确定性、有效地避免推理规则之间的相互关系问题,并且使认知结构最简推理的计算复杂度与推理节点个数成线性关系.     

Representation and Reasoning Under Uncertainty in Deception Detection: A Neuro-Fuzzy Approach

An analysis of the process and human cognitive model of deception detection (DD) shows that DD is infused with uncertainty, especially in high-stake situations. There is a recent trend toward automating DD in computer-mediated communication. However, extant approaches to automatic DD overlook the importance of representation and reasoning under uncertainty in DD. They represent uncertain cues as crisp values and can only infer whether deception occurs, but not to what extent deception occurs. Based on uncertainty theories and the analyses of uncertainty in DD, we propose a model to represent cues and to reason for DD under uncertainty, and address the uncertainty due to imprecision and vagueness in DD using fuzzy sets and fuzzy logic. Neuro-fuzzy models were developed to discover knowledge for DD. The evaluation results on five data sets showed that the neuro-fuzzy method not only was a good alternative to traditional machine-learning techniques but also offered superior interpretability and reliability. Moreover, the gains of neuro-fuzzy systems over traditional systems became larger as the level of uncertainty associated with DD increased. The findings of this paper have theoretical, methodological, and practical implications to DD and fuzzy systems research.     

证据推理规则的性质研究及方法修正

证据推理规则能有效处理证据的重要性及可靠性,针对该方法中证据权重归一化对合成结果的影响进行研究,据此分析证据合成时是否应归一化权重;探究证据推理规则与证据折扣方法之间的联系,证明两者的合成结果呈比例关系;分析证据推理规则在证据的重要性与可靠性表示上的不合理之处,并提出一种改进方法,证明了修改后的方法仍然满足4条基本合成公理．     

D-S证据理论在数据融合中的应用及改进

对证据合成的基本原则和D-S合成公式作了分析,针对D-S算法实现过程中存在的失效问题提出了解决的方法,对该理论的不足采用了修正的合成规则,通过实例运算表明该方法能够对传感器提供的不一致证据进行组合,并使组合结果更加直观.     

基于证据推理的电站锅炉空预器热点检测

准确检测空气预热器(以下简称空预器)热点对火力发电机组的安全运行具有重要意义.综合利用热电偶和红外传感器的温度信息,通过Dempster-Shafer(DS)证据理论对这两类温度信息进行融合推理,决策当前空预器内部的火情状态.为准确计算各证据体的基本概率,首先利用最小二乘支持向量机(LS-SVM)和Sigmoid函数建立多元分类器,实现对温度测点较精确的类别判断,然后根据类别票数情况计算各证据体的基本概率.实验结果表明:由最小二乘支持向量机(LS-SVM)和Sigmoid函数建立的多元分类器具有较高的分类准确率,所提出的空预器热点检测方法具有较高的判警准确率.     

A Multi-dimensional Trust-aware Cloud Service Selection Mechanism Based on Evidential Reasoning Approach

In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning(ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.     

证据推理理论及其应用

证据理论既能够灵活处理不确定信息, 包括随机性、模糊性、不准确性和不一致性, 又能够有效融合定量信息和定性知识. 目前, 证据理论已广泛应用于评估与决策等多个领域中, 包括多属性决策分析、信息融合、模式识别和专家系统等. 本文从D-S证据理论出发, 针对Dempster组合规则存在的“反直觉”问题和组合爆炸, 主要围绕置信分布理论系统地梳理了证据理论的发展过程, 总结分析了国内外典型文献, 最后从实际应用对证据理论进行了简要的评述和展望.