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感知属性权重：一种新颖的基本信念分配方法。

Sensing Attribute Weights: A Novel Basic Belief Assignment Method.

作者信息

Jiang Wen, Zhuang Miaoyan, Xie Chunhe, Wu Jun

机构信息

School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.

出版信息

Sensors (Basel). 2017 Mar 30;17(4):721. doi: 10.3390/s17040721.

DOI:10.3390/s17040721
PMID:28358325
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5421681/
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.

摘要

由于Dempster-Shafer证据理论在处理软传感器不确定性信息方面具有良好性能，因此在许多软传感器数据融合系统中得到了广泛应用。然而，如何确定基本置信分配（BBA）仍然是一个悬而未决的问题。现有的确定BBA的方法没有考虑每个属性的可靠性；同时，它们无法在开放世界中有效地确定BBA。本文基于属性权重，提出了一种不仅在封闭世界而且在开放世界中确定BBA的新方法。首先使用训练样本构建每个属性的高斯模型。其次，基于高斯隶属函数测量测试样本与属性模型之间的相似度。然后，利用类间重叠度生成属性权重。最后，根据感知到的属性权重确定BBA。几个小数据集的例子表明了该方法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/61b4ca48fda7/sensors-17-00721-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/78e3161964d4/sensors-17-00721-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/40e49288a743/sensors-17-00721-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/c324ec21626b/sensors-17-00721-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/61b4ca48fda7/sensors-17-00721-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/78e3161964d4/sensors-17-00721-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/8a1a117ac330/sensors-17-00721-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/efd9f1961d1c/sensors-17-00721-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/40e49288a743/sensors-17-00721-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/c324ec21626b/sensors-17-00721-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/52b4/5421681/61b4ca48fda7/sensors-17-00721-g006.jpg

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