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Sensing Attribute Weights: A Novel Basic Belief Assignment Method
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DOI：10.3390/s17040721.png)
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摘要
En 中文
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.
Keyword:
Dempster-Shafer evidence theory
generalized evidence theory
basic belief assignment
soft sensors data fusion
Gaussian distribution
attribute weights
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3.5
论文数:
7.3W
被引数:
20.9W
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