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Sensors 2017, 17(4), 721; doi:10.3390/s17040721

Sensing Attribute Weights: A Novel Basic Belief Assignment Method

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
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Academic Editors: Maria Gabriella Xibilia, Alex Alexandridis and Elias N. Zois
Received: 20 December 2016 / Revised: 25 March 2017 / Accepted: 27 March 2017 / Published: 30 March 2017
(This article belongs to the Special Issue Soft Sensors and Intelligent Algorithms for Data Fusion)
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Abstract

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. View Full-Text
Keywords: Dempster–Shafer evidence theory; generalized evidence theory; basic belief assignment; soft sensors data fusion; Gaussian distribution; attribute weights Dempster–Shafer evidence theory; generalized evidence theory; basic belief assignment; soft sensors data fusion; Gaussian distribution; attribute weights
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Jiang, W.; Zhuang, M.; Xie, C.; Wu, J. Sensing Attribute Weights: A Novel Basic Belief Assignment Method. Sensors 2017, 17, 721.

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