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多Agent系统存在的动态特性使证据推理中的可传递置信模型(TBM)能够有效地处理动态环境的证据推理。在分析和研究可传递置信模型算法的基础上,提出一种基于证据推理TBM模型的多Agent决策融合方法,构建多Agent决策融合系统的框架模型,分析该系统的信息更新、合成算法及决策制定算法。利用SimuroSot作为仿真平台,将该方法应用于判断对手的队形和策略,得到了较满意的结果。 相似文献
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基于MAS的复杂系统分布式求解策略与推理研究 总被引:3,自引:1,他引:3
针对复杂的连续生产线系统监测与故障诊断的动态、分布、实时和不确定特性,将多Agent及相关智能技术引入复杂故障诊断领域,提出了一种嵌套式基于消息传递的多Agent组织模型,分析了基于MAS的分布式智能故障诊断方法和过程;研究了模型系统的动态适应性和稳定性;设计了实时诊断Agent工作状态的表达机制;讨论了Agent间的协调协作机制及融合方法;给出了多Agent诊断系统诊断决策的集成描述结构;提出了任务分解与分配调度算法,以充分发挥Agent的社会性和基于场景的特点,使得在复杂系统的问题求解中能提供更为可靠的诊断结果,而且还能节约资源提高诊断效率.将其应用于某安全监控系统中,取得了与专家相似的诊断结果,克服了以往监控诊断系统存在的弊端,提高了企业的安全运行效率.与传统诊断方法相比,体现了Agent技术在复杂分布式问题求解领域的特有优势和良好前景. 相似文献
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基于分布式人工智能的思想 ,将多Agent技术引入复杂故障诊断领域 ,分析了基于MAS的分布式智能故障诊断方法和过程 ;讨论了基于模式聚类的故障求解机制及对诊断问题任务辨识、分解 ;研究了多Agent宏观上的约束和关联 ;设计了应用Agent工作状态的表达机制 ;确定了应用Agent间的工作状态影响关系及多Agent间的交互、协作和通讯 ;构建了多Agent模糊关联模型 ;给出了多Agent诊断系统局部诊断决策与全局诊断决策的集成描述结构 ;建立了一种分布式Agent诊断系统结构及其原型系统 .在某电力企 相似文献
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基于多Agent的分布式故障智能诊断原型系统研究 总被引:10,自引:0,他引:10
近年来,基于多Agent的分布式故障系统已成功地应用于众多领域,本文将多Agent技术应用于故障诊断领域研究,以开发故障智能诊断的多Agent系统,提出了基于原型系统 的基本框架和系统实现途径及方法,其中着重研究了基于多Agent理论的诊断问题分布式任务分解与控制问题,以及诊断Agent之间的协调合作问题,原型系统的研究,为实际应用系统的研制开发提供了理论指导和方法依据。 相似文献
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本文针对多Agent系统中Agent之间的盲目交互可能产生的效率低下问题,提出了一种基于慨念树结构的多Agent合作求解模型.在这个模型中,各Agent基于自己的领域知识构造出概念树,通过Agent之间的合作,对概念树从根节点开始使用证据理论实现逐层聚焦,逐步缩小求解范围.为此,本文基于模态、逻辑和关系概念提出了一种面向可能解集的证据理论表示,并探讨了在多Agent环境下应用证据理论可能导致的若干问题. 相似文献
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在故障诊断中引入多Agent并行推理、独立求解的机制能够提高诊断准确率。通过特征提取降低特征维数,形成子任务,并分配给具有专家能力的Agent处理。然而多数情况下不同Agent的诊断结论会存在差异,为解决该问题,引入D-S证据理论将各Agent的结论作为多源证据进行融合。在融合中引入可信度分配矩阵表征Agent能力。最后通过一个诊断实例说明了此方法,通过仿真对比实验验证了此方法的有效性。 相似文献
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自主规划、适应环境和学习功能等多项智能行为是现代战争的决策活动对辅助决策系统的基本要求.以常规导弹兵力行动方案的辅助决策为背景,提出了基于多Agent的系统总体设计,确立了系统构成的核心:机动方案Agent、测试方案Agent、运输方案Agent、保障方案Agent、,集成方案Agent以及任务管理Agent,建立了各类Agent的结构模型,并阐述了其功能. 相似文献
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A new combination rule based on Dezert-Smarandache theory (DSmT) is proposed to deal with the conflict evidence resulting from the non-exhaustivity of the discernment frame. A two-dimensional measure factor in Dempster-Shafer theory (DST) is extended to DSmT to judge the conflict degree between evidence. The original DSmT combination rule or new DSmT combination rule can be selected for fusion according to this degree. Finally, some examples in simultaneous fault diagnosis of motor rotor are given to illustrate the effectiveness of the proposed combination rule. 相似文献
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He Luo Shan-lin Yang Xiao-jian Hu Xiao-xuan Hu 《Expert systems with applications》2012,39(3):2524-2531
Multi sensors fusion is a very important process for fault diagnosis system. Information obtained from multi sensors need to be fused because no single sensor can get all the information for fault diagnosis. Moreover, information from different sensors may be uncertainty, inaccuracy, or even conflicting. Evidence theory can be used for information fusion, which is regarded as an extension form of Bayesian reasoning, but it has a better fusion result by simple reasoning process using belief function without knowing the prior probability. All the information collected from multi sensors in the system can be described as the evidence for diagnosis so that the fault diagnosis problem can then be modeled as a problem of evidence fusion and decision. In this paper, the classical Dempster-Shafer evidence theory is discussed, and the disadvantages of the combination rule are also analyzed. The notion of support degree of focal element is suggested in order to evaluate the conflicts between multi sensors. The new combination rule is then built to allocate the conflicted information from multi sensors based on the support degree of focal element. Furthermore, the decision rules for fault diagnosis are also proposed, as well as the architecture of the agent oriented intelligent fault diagnosis system. Finally, a case study is given to illustrate the performance of the proposed model. 相似文献
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提出一种将诊断证据静态融合与动态更新相结合的故障诊断方法.在静态融合阶段,利用Dempster组合规则融合每个时刻的多条局部诊断证据,获取静态融合证据,并给出基于证据距离的故障信度静态收敛指标;在动态更新阶段,基于条件化的线性组合更新规则,利用当前时刻静态融合证据更新历史证据,获取更新后的全局性诊断证据,并给出基于S函数的故障信度动态收敛指标.在两个阶段中,基于静态和动态信度收敛性指标函数,分别给出相应的优化学习方法,获取静态融合中局部诊断证据的静态折扣系数、动态更新中历史与当前证据的更新权重系数等参数的最优值.在最大信度原则下,利用更新后获取的诊断证据做出诊断决策.最后,通过在电机柔性转子实验台上的诊断实验,将所提方法与已有的典型融合诊断方法进行了对比分析,说明所提出的融合诊断方法及其性能指标函数和参数优化方法的有效性. 相似文献
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周頔 《计算机测量与控制》2018,26(9):5-8
为了提高不完备信息系统故障诊断的正确性与效率,本文提出一种基于粗糙集理论、蚁群优化算法和RBF神经网络相结合的故障智能诊断方法。该方法首先利用“条件组合补齐算法”对不完备的数据进行完备化处理,再利用粗糙集对条件属性进行知识约简,得到具有最大完备度的最小规则集,接着用蚁群算法优化RBF神经网络的权值,并将最小规则集用于训练RBF神经网络模型,获得故障智能诊断模型。通过实际工程数据验证故障智能诊断模型的有效性,结果表明提出的方法能有效实现系统故障的诊断。 相似文献
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Dexter A.L. Benouarets M. 《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):673-682
A new fuzzy-model-based approach to fault detection and diagnosis is proposed. The scheme uses a set of fuzzy reference models which describe faulty and fault-free operation, and a classifier based on fuzzy matching for fault diagnosis. The reference models are obtained off-line from simulation data. A fuzzy model which describes the actual behavior of the plant is identified online from normal operating data and compared to each of the reference models. A degree of similarity is evaluated every time the online fuzzy model is identified. Dempster's rule of combination is used to combine new evidence with that already collected. The method of diagnosis accounts for any ambiguity (which may result from fault-free and faulty operation, or different faults, having similar symptoms at a given operating point) by comparing the fuzzy reference models with each other. Results are presented which demonstrate the effectiveness of the scheme when it is used to detect and identify faults in the cooling coil subsystem of the air-handling unit of both simulated and experimental air-conditioning plant 相似文献
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In this paper, we extend the original belief rule-base inference methodology using the evidential reasoning approach by i) introducing generalised belief rules as knowledge representation scheme, and ii) using the evidential reasoning rule for evidence combination in the rule-base inference methodology instead of the evidential reasoning approach. The result is a new rule-base inference methodology which is able to handle a combination of various types of uncertainty.Generalised belief rules are an extension of traditional rules where each consequent of a generalised belief rule is a belief distribution defined on the power set of propositions, or possible outcomes, that are assumed to be collectively exhaustive and mutually exclusive. This novel extension allows any combination of certain, uncertain, interval, partial or incomplete judgements to be represented as rule-based knowledge. It is shown that traditional IF-THEN rules, probabilistic IF-THEN rules, and interval rules are all special cases of the new generalised belief rules.The rule-base inference methodology has been updated to enable inference within generalised belief rule bases. The evidential reasoning rule for evidence combination is used for the aggregation of belief distributions of rule consequents. 相似文献