Sensing Attribute Weights: A Novel Basic Belief Assignment Method.
Sensing Attribute Weights: A Novel Basic Belief Assignment Method.
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感知属性权重：一种新颖的基本置信分配方法
DOI：
10.3390/s17040721
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发表时间：
2017-03-30
期刊：
影响因子：
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通讯作者：
Wu J
中科院分区：
文献类型：
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作者：
Jiang W;Zhuang M;Xie C;Wu J
Dempster-Shafer证据理论因其在处理软测量不确定性信息方面的良好性能而被广泛应用于许多软测量数据融合系统中。然而，如何确定基本信念分配(BBA)仍然是一个有待解决的问题。现有的确定BBA的方法没有考虑每个属性的可靠性，同时也不能在开放世界中有效地确定BBA。本文提出了一种新的基于属性权重的BBA确定方法，该方法不仅适用于封闭世界，也适用于开放世界。首先利用训练样本建立每个属性的高斯模型。其次，基于高斯隶属函数度量测试样本与属性模型之间的相似度。然后，利用类之间的重叠度生成属性权重。最后，根据感测到的属性权重确定BBA。几个小数据集的算例表明了该方法的有效性。
Dempster–Shafer evidence theory is widely used in many soft sensors data fusion systems on account of its good performance for handling the uncertainty information of soft sensors. However, how to determine basic belief assignment (BBA) is still an open issue. The existing methods to determine BBA do not consider the reliability of each attribute; at the same time, they cannot effectively determine BBA in the open world. In this paper, based on attribute weights, a novel method to determine BBA is proposed not only in the closed world, but also in the open world. The Gaussian model of each attribute is built using the training samples firstly. Second, the similarity between the test sample and the attribute model is measured based on the Gaussian membership functions. Then, the attribute weights are generated using the overlap degree among the classes. Finally, BBA is determined according to the sensed attribute weights. Several examples with small datasets show the validity of the proposed method.