@inproceedings{256e9537ab064157a2cea8fa91219051,
title = "Diagnosis technique based on BP and D-S theory",
abstract = "This paper constructs a common data fusion framework of fault diagnosis, by combining local neural networks with dempster-shafer (D-S) evidential theory. The RBF neural network is proposed as a local neural network of the fault pattern recognition, and its input vectors are extracted by the wavelet packet decomposition of various frequency energy. Then, the signal of each sensor separately has a feature level fusion. This method is effective, verified by experiments. The given decision level fusion is based on combining the features of the neural network and the D-S theory, and experiments show the results of the fault diagnosis are more accurate by this method.",
keywords = "D-S theory, Data fusion, Fault diagnosis, RBP algorithm",
author = "Mo, \{Qiu Yun\} and Jie Cao and Feng Gao",
year = "2011",
doi = "10.4028/www.scientific.net/AMR.179-180.544",
language = "English",
isbn = "9783037850169",
series = "Advanced Materials Research",
pages = "544--548",
booktitle = "Materials Science and Engineering",
note = "2010 International Conference on Materials Science and Engineering Science, ICMSES 2010 ; Conference date: 11-12-2010 Through 12-12-2010",
}