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针对旋转机械故障诊断中的不确定性问题,提出基于多传感器D-S(Dempster-shfer)证据理论和模糊数学相结合的信息融合算法;通过多传感器测出旋转机械振动位移和振动加速度,得出D-S证据理论中多传感器分别对旋转机械的信度函数分配值,使用改进的D-S证据算法得到融合后的信度函数分配值,由D-S合成规则确定故障类型,通过在多功能旋转机械平台上的试验得出改进后的证据理论明显提高了旋转机械故障诊断的精度. 相似文献
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针对电动汽车电池系统的故障采用基于神经网络的改进D-S证据理论组合规则完成诊断过程。为了避免单一途径的诊断可能造成故障漏检误检的状况,决策层采用D-S证据理论组合规则来确定基于BP网络和RBF网络两种故障诊断算法结果。然而为了克服D-S证据理论处理高度冲突证据的缺陷,本文提出了一种基于神经网络改进的D-S证据理论组合规则。首先,采用神经网络对电池故障进行初步诊断,结合网络诊断准确率来分配不确定信息并构造证据体,又引入了证据间的支持矩阵来确定新的加权证据体。然后,把各个焦元的信任度融入D-S证据理论组合规则,从而融合神经网络证据体及新加权证据体。最后,依据决策准则确定锂电池系统的故障状态。通过仿真实验验证了本文提出的改进D-S证据理论融合诊断方法在电动汽车锂电池故障诊断中的有效性。 相似文献
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针对基于多传感器信息融合的煤矿带式输送机健康诊断方法运用D-S证据理论在处理冲突证据时失效的问题,提出了一种基于模糊证据理论的带式输送机健康诊断方法。该方法首先利用多种传感器采集带式输送机信息,并根据隶属度函数获取基本概率赋值,从而提取信息特征;然后通过对冲突证据进行修正并应用D-S证据理论的合成规则,实现基于模糊证据理论的信息融合;最后根据决策规则判断带式输送机运行状态。通过实例验证了该方法的有效性。 相似文献
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针对多分类器决策融合研究中利用有限的训练数据对分类器概率参数估计时存在较大偏差的问题,提出一种基于D-S证据推理(ER)的多分类器决策融合算法。利用不确定性描述分类器性能,并针对D-S组合规则在分类器结果高冲突情形下易出现决策融合悖论的问题,提出基于分类器信度加权融合算法实现流量识别决策融合。实验结果表明,多数投票法和Bayes最大后验概率法识别准确率分别为78.3%和81.7%,证据推理决策融合的识别准确率提高到82.2%~91.6%,而拒识率则保持在4.1%~6.2%。 相似文献
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文章提出了一种基于粗糙集和证据理论的目标识别方法.首先对决策表进行约简得出决策规则,然后计算规则可信度分配和规则权重,最后根据改进的D-S合成规则进行规则合成,得到识别结果. 相似文献
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基于预处理模式的D-S证据理论改进方法 总被引:2,自引:0,他引:2
D-S证据理论是决策融合的主要方法之一,但典型的D-S理论不大适应高冲突证据组合.本文提出一种基于预处理模式的方法,在利用Dempster组合规则进行证据组合之前,将冲突焦元的基本概率赋值部分转移到焦元并集,采用证据之间的冲突额度来确定证据组合顺序.由于该方法将冲突化解为不确定的知识表示,可以处理冲突证据的组合问题. 相似文献
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对于一致决策表,基于D-S证据理论的知识约简与代数约简所得的结果是一致的,对于不一致决策表,它们并不完全一致.本文通过具体算例说明了基于D-S证据理论的广义决策约简与代数约简的在不一致决策表下的差异性.理论上证明了广义决策约简仅与分配约简是等价的,提出一种基于D-S证据理论求代数约简的方法.理论分析和算例都证明了本文方法的正确性. 相似文献
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Tong Luo Kramer K. Goldgof D.B. Hall L.O. Samson S. Remsen A. Hopkins T. 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2004,34(4):1753-1762
We present a system to recognize underwater plankton images from the shadow image particle profiling evaluation recorder (SIPPER). The challenge of the SIPPER image set is that many images do not have clear contours. To address that, shape features that do not heavily depend on contour information were developed. A soft margin support vector machine (SVM) was used as the classifier. We developed a way to assign probability after multiclass SVM classification. Our approach achieved approximately 90% accuracy on a collection of plankton images. On another larger image set containing manually unidentifiable particles, it also provided 75.6% overall accuracy. The proposed approach was statistically significantly more accurate on the two data sets than a C4.5 decision tree and a cascade correlation neural network. The single SVM significantly outperformed ensembles of decision trees created by bagging and random forests on the smaller data set and was slightly better on the other data set. The 15-feature subset produced by our feature selection approach provided slightly better accuracy than using all 29 features. Our probability model gave us a reasonable rejection curve on the larger data set. 相似文献
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Fuzzy decision trees can be used to generate fuzzy rules from training instances to deal with forecasting and classification problems. We propose a new method to construct fuzzy decision trees from relational database systems and to generate fuzzy rules from the constructed fuzzy decision trees for estimating null values, where the weights of attributes are used to derive the values of certainty factors of the generated fuzzy rules. We use the concept of "coefficient of determination" of the statistics to derive the weights of the attributes in relational database systems and use the normalized weights of the attributes to derive the values of certainty factors of the generated fuzzy rules. Furthermore, we also use regression equations of the statistics to construct a complete fuzzy decision tree for generating better fuzzy rules. The proposed method obtains a higher average estimated accuracy rate than the existing methods for estimating null values in relational database systems. 相似文献
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This paper presents a real-time fuzzy expert system to scheduling parts for a flexible manufacturing system (FMS). First, some vagueness and uncertainties in scheduling rules are indicated and then a fuzzy-logic approach is proposed to improve the system performance by considering multiple performance measures. This approach focuses on characteristics of the system's status, instead of parts, to assign priorities to the parts waiting to be processed. Secondly, a simulation model is developed and it has shown that the proposed fuzzy logic-based decision making process keeps all performance measures at a good level. The proposed approach provides a promising alternative framework in solving scheduling problems in FMSs, in contrast to traditional rules, by making use of intelligent tools. 相似文献
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A Bayesian decision model for cost optimal record matching 总被引:1,自引:0,他引:1
V.S. Verykios G.V. Moustakides M.G. Elfeky 《The VLDB Journal The International Journal on Very Large Data Bases》2003,12(1):28-40
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E A Gilpin R A Olshen K Chatterjee J Kjekshus A J Moss H Henning R Engler A R Blacky H Dittrich J Ross 《Computers and biomedical research》1990,23(1):46-63
Whether decision rules derived statistically from patient data can produce better decisions than an expert clinician or a model of the expert clinician (expert system) is controversial. We examined this issue in the context of predicting cardiac death by 1 year for patients discharged from the hospital following acute myocardial infarction. Decision rules were derived from a base sample of 781 patients. These decision rules and three experienced cardiologists then estimated probability of death by 1 year for each patient in a separate test sample (n = 400). In our evaluation of the performance of the decision rules and physicians, we detected no differences, although the decision rules and physicians tended to classify the patients somewhat differently. Further multivariate analyses on the physicians' predictions showed that two of the physicians paid attention to somewhat different variables than the third physician. Lack of agreement among expert cardiologists would complicate modeling of a consensual decision-making process within the framework of an expert system. 相似文献
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Jae Kwon Bae 《Expert systems with applications》2012,39(10):9159-9165
The financial distress forecasting has long been of great interest both to scholars and practitioners. The financial distress forecasting is basically a dichotomous decision, either being financial distress or not. Most statistical and artificial intelligence methods estimate the probability of financial distress, and if this probability is greater than the cutoff value, then the prediction is to be financial distress. To improve the accuracy of the financial distress prediction, this paper first analyzed the yearly financial data of 1888 manufacturing corporations collected by the Korea Credit Guarantee Fund (KODIT). Then we developed a financial distress prediction model based on radial basis function support vector machines (RSVM). We compare the classification accuracy performance between our RSVM and artificial intelligence techniques, and suggest a better financial distress predicting model to help a chief finance officer or a board of directors make better decision in a corporate financial distress. The experiments demonstrate that RSVM always outperforms other models in the performance of corporate financial distress predicting, and hence we can predict future financial distress more correctly than any other models. This enhancement in predictability of future financial distress can significantly contribute to the correct valuation of a company, and hence those people from investors to financial managers to any decision makers of a company can make use of RSVM for the better financing and investing decision making which can lead to higher profits and firm values eventually. 相似文献
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《Computers & Industrial Engineering》1999,37(3):527-541
Multicriteria decision making models are characterized by the need to evaluate a finite set of alternatives with respect to multiple criteria. The criteria weights in different aggregation rules have different interpretations and implications which have been misunderstood and neglected by many decision makers and researchers. By analyzing the aggregation rules, identifying partial values, specifying explicit measurement units and explicating direct statements of pairwise comparisons of preferences, we identify several plausible interpretations of criteria weights and their appropriate roles in different multicriteria decision making models. The underlying issues of scale validity, commensurability, criteria importance and rank consistency are examined. 相似文献
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A probabilistic reasoning-based decision support system for selection of remediation technologies for petroleum-contaminated sites 总被引:1,自引:0,他引:1
Selection of remediation technologies for petroleum-contaminated sites is difficult given the large number of technologies available and inherent uncertainties involved in the selection process. In this paper, we explore the use of an inexact algorithm for probability reasoning for dealing with the uncertainties involved in the problem. By incorporating domain knowledge as well as the stochastic uncertainty, a probabilistic rule-based decision support system (PDSS) has been developed to support the decision making process. The system has been applied to two case studies, in which the best option of remediation technology can be determined according to calculated probability values. In comparison to deterministic and fuzzy decision support systems, the PDSS can provide a recommendation together with a measure on the reliability or degree to which the recommended decision can be trusted. 相似文献