DocumentCode :
2244719
Title :
Outlier identify based on BP neural network in dam safety monitoring
Author :
Li, Ning ; Li, Peng ; Xinling Shi ; Yan, Kai ; Ren, Wenping
Author_Institution :
Sch. of Inf. Sci. & Eng., Yunnan Univ., Kunming, China
Volume :
2
fYear :
2010
fDate :
6-7 March 2010
Firstpage :
210
Lastpage :
214
Abstract :
In popular outlier processing methods, some emphasize on spotted outliers processing and some emphasize on isolated outliers processing. They have seldom processed outliers from the perspective of outlier producing mechanism. This paper aims at the problem of outliers in dam safety monitoring and an outlier identify method which based on BP neural network is presented. This method based on the mechanism of the dam monitoring data formation firstly created the BP neural network predicting model of monitoring data, then identify the outliers. The simulation results indicated that this method works with spotted outliers and isolated outliers and this method has a unique advantage on analysis of the outlier causes.
Keywords :
backpropagation; condition monitoring; dams; geotechnical engineering; neural nets; structural engineering computing; BP neural network predicting model; dam safety monitoring; outlier identify method; outlier processing methods; Additive noise; Labeling; MIMO; Monitoring; Neural networks; Rayleigh channels; Receiving antennas; Safety; Transmitters; Transmitting antennas; BP neural network; dam safety monitoring; outlier identifying; prediction model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on
Conference_Location :
Wuhan
ISSN :
1948-3414
Print_ISBN :
978-1-4244-5192-0
Electronic_ISBN :
1948-3414
Type :
conf
DOI :
10.1109/CAR.2010.5456564
Filename :
5456564
Link To Document :
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