Paper Titles
The Broyden Method for M-P-Φ Relationship of Beam-Column with Residual Stresses
p.1071
A Software of Pre-Buckling Analysis of Laminated Plates and Shells Based on Stiffness Matrix Determinant
p.1075
Structural Analysis and Improvement of Large-Scale Forklift's Wheel Rim
p.1080
Based on Micromechanical Damage of Experimental Study on 20 Steel Notched Specimen's Acoustic Emission Characteristics
p.1084
The Progressive Optimization Model on the Corrosion Rate of the Grounding Grid
p.1090
Research and Application of Material Corrosion Prevention Technology in the Completed well System for Sour Gas well
p.1096
Solidworks Engineering Drawing Automatically Adjust Technology and Implementation
p.1103
Research of 3D Parametric Design System of Worm Drive Based on Pro/E
p.1107
The Study of ANSYS-Based Finite Element Calculation Model for Push-Back Rack
p.1111

The Progressive Optimization Model on the Corrosion Rate of the Grounding Grid

Article Preview

Abstract:

Established a cross-validation of the model optimization algorithm in order to predict the corrosion rate of the grounding grid. Forward neural network input variables by principal component analysis to extract the main element, eliminating the correlation between variables; cross-validation method and change the termination of the neural network training conditions, the selection of network training model. The final simulation results show that, get a good grounding grid corrosion rate stability and generalization performance optimization prediction model can predict the corrosion rate of the grounding grid.

You might also be interested in these eBooks
Advances in Manufacturing Science and Engineering View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 712-715)

Pages:

1090-1095

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.712-715.1090

Citation:

Cite this paper

Online since:

June 2013

Authors:

Fen Lei Dai, Wen Qiang Xie

Keywords:

BP Neural Network (BPNN), Corrosion Rate Prediction, Cross-Validation, Model Optimization, Principal Component Analysis (PCA)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

References

[1] Liu Liqiang, Luo Xianjue. Diagnosis grounding grid corrosion block hybrid optimization algorithm [J]. Electrical Engineering, 2009,29 (7) :33-38.

Google Scholar

[2] Niu Tao, Luo Xianjue, Wang Sen.Fault diagnosis of grounding grid corrosion testability analysis [J]. Transactions of China Electrotechnical Society, 2010,25 (6) :192-198.

Google Scholar

[3] Liu Jian,Wang Shuqi.Corrosion Diagnosis grounding grid testability [J] High Voltage Engineering, 2008,34 (1) :64-69.

Google Scholar

[4] Zhao Bingjun, Zheng Hong-Wei. Hengshui area substation ground grid corrosion analysis [J]. Hebei Electric Power Technology, 2008,27 (6) :47-49.

Google Scholar

[5] Fu Longhai,Wu Guangning. Qinghai-Tibet railway substation ground grid corrosion cracking point judgment [J] Electric Power System and Automation, 2006, 18 (1) :20-23.

Google Scholar

[6] Liu Yugen,Wu Lixiang. Corrosion to optimize diagnostic utility analysis [J]. Chongqing University, 2008,31 (4) :417-420.

Google Scholar

[7] Ma Wenjing,Zhang bo. Corrosion diagnosis method based on the regularization method of least squares base mesh research [J]. Heilongjiang Electric Power, 2012,34 (4) :278-284.

Google Scholar

[8] Liu Yugen,Teng Yongxi. Grounding grid corrosion diagnosis research [J]. High Voltage Engineering, 2004,30 (6) :19-21.

Google Scholar

[9] Wang Shuaihua,Qin Xiaoxia. MATLAB neural network in pipeline soil corrosion evaluation [J] Oil and gas storage and transportation, 2009,28 (11) :57-59.

Google Scholar

[10] Wang Wan,Zhang Junming. Buried gas pipeline corrosion remaining life based on BP artificial neural network theory [J] Modern Machinery, 2010 (3) :44-46.

Google Scholar

[11] Qu LiangShan, Li Xiaogang.By BP artificial neural network method build carbon steel Regional soil corrosion prediction model [J] Journal of Beijing University of Science and Technology, 2009,31 (12) :1569-1575.

Google Scholar