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A target identification comparison of Bayesian and Dempster-Shafermultisensor fusion 总被引:2,自引:0,他引:2
Buede D.M. Girardi P. 《IEEE transactions on systems, man, and cybernetics. Part A, Systems and humans : a publication of the IEEE Systems, Man, and Cybernetics Society》1997,27(5):569-577
This paper demonstrates how Bayesian and evidential reasoning can address the same target identification problem involving multiple levels of abstraction, such as identification based on type, class, and nature. In the process of demonstrating target identification with these two reasoning methods, we compare their convergence time to a long run asymptote for a broad range of aircraft identification scenarios that include missing reports and misassociated reports. Our results show that probability theory can accommodate all of these issues that are present in dealing with uncertainty and that the probabilistic results converge to a solution much faster than those of evidence theory 相似文献
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针对证据网络推理方法无法对区间规则进行表示和推理的问题, 提出一种基于区间规则的条件证据网络推理决策方法. 该方法针对模糊规则的条件概率或信度为不确定区间的情况, 可同时表达不确定性和模糊性; 并将区间不确定规则转化为区间条件信度函数作为证据网络的结点参数, 通过条件推理和证据融合得到条件证据网络中各结点幂集空间中焦元的随机分布作为决策依据. 最后, 通过空中目标态势评估实例, 验证了所提出方法的有效性.
相似文献6.
针对多属性群决策中可解释性证据融合推理的实体异构性问题,给出了一个实体异构性下证据链融合推理的多属性群决策方法.基于证据推理理论,引入证据链关联的概念,从多数据表提供的数据矩阵中获取可区分的近邻证据集,推导了各数据表的相似度矩阵,并构建半正定矩阵的二次优化模型,共享群决策专家的经验知识.使用Dempster正交规则,论证了异构实体之间可解释性推理中可信度融合的合理性,并使用证据融合规则集成各个数据表的近邻证据中获得的可信度,验证了调和多源异构数据中不一致信息的有效性.通过具有实体异构性的心脏病多决策数据诊断实例说明了方法的可行性与合理性. 相似文献
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针对采用传统故障诊断方法进行电子电路故障元件诊断存在不确定性问题,从DS证据理论的基本概念和证据的融合推理方法出发,提出了采用多信息融合进行电路故障诊断的新方法.该方法通过测量待诊断电路中元件的工作温度、电压这两个参数,获取传感器对待诊断元件的信度函数,然后利用DS联合规则得出融合信度函数,进而确定故障元件.故障诊断实例的结果表明,诊断结论的可信度明显提高,不确定性明显减小,该诊断方法较传统方法更准确有效. 相似文献
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从D—S证据理论的基本概念和证据的融合推理方法出发.针对D—S证据理论中高度冲突证据的合成问题,引入证据强度的概念,提出一种修正的合成方法,并将其应用于某武器电子电路的故障诊断。通过该实例表明,与D—S、Yager、邓勇等方法相比,提出的改进方法更好地处理高冲突证据,且具有更好的解释性。 相似文献
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Deployment is a fundamental issue in Wireless Sensor Networks (WSNs). Indeed, the number and locations of sensors determine the topology of the WSN, which will further influence its performance. Usually, the sensor locations are precomputed based on a “perfect” sensor coverage model. However, in reality, there is an inherent uncertainty and imprecision associated with sensor readings. This issue impinges upon the success of any WSN deployment, and it is therefore important to consider it at the design stage. In contrast to existing work, this paper investigates the belief functions theory to design a unified approach for robust uncertainty-aware WSNs deployment. Specifically, we address the issue of handling uncertainty and information fusion for an efficient WSNs deployment by exploiting the belief functions reasoning framework. We present a flexible framework for target/event detection within the transferable belief model. Using this framework, we propose uncertainty-aware deployment algorithms that are able to determine the minimum number of sensors as well as their locations in such a way that full area coverage is provided. Related issues, such as connectivity, preferential coverage, challenging environments and sensor reliability, are also discussed. Simulation results, based on both synthetic data set and data traces collected in a real deployment for vehicle detection, are provided to demonstrate the ability of our approach to achieve an efficient WSNs deployment by exploiting a collaborative target/event detection scheme. Using the devised approach, we successfully deploy an experimental testbed for motion detection. The obtained results are reported, supported by comparison with other works. 相似文献
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In the evidential reasoning approach of decision theory, different evidence weights can generate different combined results. Consequently, evidence weights can significantly influence solutions. In terms of the “psychology of economic man,” decision-makers may tend to seek similar pieces of evidence to support their own evidence and thereby form alliances. In this paper, we extend the concept of evidential reasoning (ER) to evidential reasoning based on alliances (ERBA) to obtain the weights of evidence. In the main concept of ERBA, pieces of evidence that are easy for decision-makers to negotiate are classified in the same group or “alliance.” On the other hand, if the pieces of evidence are not easy to negotiate, they are classified in different alliances. In this study, two negotiation optimization models were developed to provide relative importance weights based on intra- and inter-alliance evidence features. The proposed models enable weighted evidence to be combined using the ER rule. Experimental results showed that the proposed approach is rational and effective. 相似文献
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The Bayesian approach is widely used in automatic target recognition (ATR) systems based on multisensor fusion technology.
Problems in data fusion systems are complex by nature and can often be characterized by not only randomness but also fuzziness.
However, in general, current Bayesian methods can only account for randomness. To accommodate complex natural problems with
both types of uncertainties, it is profitable to improve the existing approach by incorporating fuzzy theory into classical
techniques. In this paper, after representing both the individual attribute of the target in the model database and the sensor
observation or report as the fuzzy membership function, a likelihood function is constructed to deal with fuzzy data collected
by each sensor. A similarity measure is introduced to determine the agreement degree of each sensor. Based on the similarity
measure, a consensus fusion approach (CFA) is developed to generate a global likelihood from the individual attribute likelihood
for the whole sensor reports. A numerical example is illustrated to show the target recognition application of the fuzzy-Bayesian
approach.
The text was submitted by the authors in English. 相似文献
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基于D-S证据理论的传感器网络数据融合算法 总被引:1,自引:0,他引:1
在传感器网络中,多个传感器对于同一目标的识别结果经常会发生冲突,本文采用基于Dempster—Sharer证据推理理论的数据融合方法来解决这一问题。然而,采用D—S证据组合公式计算融合结果,计算量过于巨大,对处理能力有限的感知结点来说负担过重,此外,计算所造成的延时也将严重影响系统的实时性和同步性.本文提出了一个基于矩阵分析的快速融合算法,该算法采用了D—S证据理论的思想,计算得到的融合结果与D—S证据组合公式计算得到的融合结果相同.本文用数学归纳法证明了这一结论,经过模拟实验验证,和直接采用D—S证据组合公式相比,该算法的计算量和所需的计算时间明显减少. 相似文献
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为解决多平台协同数据融合问题,采用基于D-S证据理论的数据融合方法,分析了该理论在多平台协同数据融合中的应用原理,并将此方法运用于舰船类型的识别。通过MATLAB仿真计算对D-S方法在多平台协同数据融合和单平台数据融合中的应用进行了比较,验证了该理论在多平台协同数据融合中的有效性和实用性。 相似文献
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基于粗集理论的知识系统证据推理研究 总被引:11,自引:0,他引:11
证据推理是处理不确定问题的重要方法,但灾用中存在许多问题,如假设的基本概率指派(bpa)往往由专家事先确定,带有较强的主观性,基于证据推理和粗集理论的基本关系,利用粗集约简,决策表确定基本概率指派等方法解决上述问题,并在此基础上,提出了一种决策表的证据推理方法,用于决策表的预测,实例表明,证据推理和粗集理论的结合可提高并对不确定问题的求解能力。 相似文献
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On combining classifier mass functions for text categorization 总被引:4,自引:0,他引:4
Bell D.A. Guan J.W. Bi Y. 《Knowledge and Data Engineering, IEEE Transactions on》2005,17(10):1307-1319
Experience shows that different text classification methods can give different results. We look here at a way of combining the results of two or more different classification methods using an evidential approach. The specific methods we have been experimenting with in our group include the support vector machine, kNN (nearest neighbors), kNN model-based approach (kNNM), and Rocchio methods, but the analysis and methods apply to any methods. We review these learning methods briefly, and then we describe our method for combining the classifiers. In a previous study, we suggested that the combination could be done using evidential operations and that using only two focal points in the mass functions gives good results. However, there are conditions under which we should choose to use more focal points. We assess some aspects of this choice from an reasoning perspective and suggest a refinement of the approach. 相似文献
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本文提出了一种基于信息融合的动态网络安全态势评估方法。此方法采用信息融合系统的三层结构,数据层用粗糙集理论确定有用属性及权值,定时收集多源数据并标准化,特征层采用模糊聚类分析进行信息预分类,决策层用模糊识别和D-S证据理论组合的识别方法得到最终结果。 相似文献
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Jeroen Keppens 《Artificial Intelligence and Law》2012,20(2):109-143
Bayesian networks (BN) and argumentation diagrams (AD) are two predominant approaches to legal evidential reasoning, that are often treated as alternatives to one another. This paper argues that they are, instead, complimentary and proposes the beginnings of a method to employ them in such a manner. The Bayesian approach tends to be used as a means to analyse the findings of forensic scientists. As such, it constitutes a means to perform evidential reasoning. The design of Bayesian networks that accurately and comprehensively represent the relationships between investigative hypotheses and evidence remains difficult and sometimes contentious, however. Argumentation diagrams are representations of reasoning, and are used as a means to scrutinise reasoning (among other applications). In evidential reasoning, they tend to be used to represent and scrutinise the way humans reason about evidence. This paper examines how argumentation diagrams can be used to scrutinise Bayesian evidential reasoning by developing a method to extract argument diagrams from BN. 相似文献
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不确定性推理方法是人工智能领域的一个主要研究内容,If-then规则是人工智能领域最常见的知识表示方法. 文章针对实际问题往往具有不确定性的特点,提出基于证据推理的确定因子规则库推理方法.首先在If-then规则的基础上给出确定因子结构和确定因子规则库知识表示方法,该方法可以有效利用各种类型的不确定性信息,充分考虑了前提、结论以及规则本身的多种不确定性. 然后,提出了基于证据推理的确定因子规则库推理方法. 该方法通过将已知事实与规则前提进行匹配,推断结论并得到已知事实条件下的前提确定因子;进一步,根据证据推理算法得到结论的确定因子. 文章最后,通过基于证据推理的确定因子规则库推理方法在UCI数据集分类问题的应用算例,说明该方法的可行性和高效性. 相似文献