DocumentCode :
508217
Title :
Deformation Prediction of Transmission Pole Foundation by Using Improved BP Neural Network
Author :
Yong, Zhang ; Yunyun, Zhao
Author_Institution :
Hebei Univ. of Eng., Handan, China
Volume :
3
fYear :
2009
fDate :
14-16 Aug. 2009
Firstpage :
607
Lastpage :
611
Abstract :
Based on the field survey data of the goaf along the UHV path and by including the main geological and mining factors, the stability of UHV transmission pole foundation via Shanxi goaf have been analyzed in details. Using BP artificial neural network method, the paper set up the prediction model of subsidence deformation of pole foundation above the goaf through experiment and study of the data samples. Levenberg-Marquardt algorithm was applied in order to achieve better results. It is concluded that by using BP neural network model, predicting pole foundation stability of the goaf is convenient, reliable, and more applicable.
Keywords :
backpropagation; neural nets; poles and towers; power engineering computing; Levenberg-Marquardt algorithm; Shanxi goaf; UHV transmission pole foundation; deformation prediction; improved BP neural network; Artificial neural networks; Computer networks; Deformable models; Geology; Neural networks; Nonlinear distortion; Predictive models; Stability; Surface cracks; Transmission lines; 1000kV UHV; BP neural network; goaf; pole foundation stability prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
Type :
conf
DOI :
10.1109/ICNC.2009.474
Filename :
5366018
Link To Document :
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