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