TY - JOUR
T1 - Sensing Attribute Weights
T2 - A novel basic belief assignment method
AU - Jiang, Wen
AU - Zhuang, Miaoyan
AU - Xie, Chunhe
AU - Wu, Jun
N1 - Publisher Copyright:
© 2017 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2017/4
Y1 - 2017/4
N2 - 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.
AB - 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.
KW - Attribute weights
KW - Basic belief assignment
KW - Dempster–Shafer evidence theory
KW - Gaussian distribution
KW - Generalized evidence theory
KW - Soft sensors data fusion
UR - http://www.scopus.com/inward/record.url?scp=85016571823&partnerID=8YFLogxK
U2 - 10.3390/s17040721
DO - 10.3390/s17040721
M3 - 文章
C2 - 28358325
AN - SCOPUS:85016571823
SN - 1424-8220
VL - 17
JO - Sensors
JF - Sensors
IS - 4
M1 - 721
ER -