Fault Diagnostic Method of High-Current Converter Using Wavelet Neural Network Based on Improved Adaptive Genetic Algorithms

Te Fang Chen; Qiang Fu; Jiao Jiao Zhu

doi:10.4028/www.scientific.net/AMR.466-467.1150

Paper Titles

PWM Speed Regulating System of DC Torque Motor Based on Fuzzy Immune-PID Control
p.1129

Knowledge Based Engineering in Complicated Product Design
p.1135

Study and Design of a 3-Lamp Cold Light Lifting Retractor
p.1140

Refactoring of Mechanical Model Simulation Software Based on Component Technology
p.1145

Fault Diagnostic Method of High-Current Converter Using Wavelet Neural Network Based on Improved Adaptive Genetic Algorithms
p.1150

The Design and Simulation of Low-Cost and Super-Applicability Flight Control System of Super-Size UAV
p.1156

Analysis of the Solenoid Driving Technique for DCT
p.1162

Design on Remote Intelligent Terminal Unit of Industrial Ethernet Based on STM32
p.1167

Design of Flow Simulation Subsystem of Ground Simulation Test for Aircraft Fuel System
p.1172

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 466-467Fault Diagnostic Method of High-Current Converter...

Fault Diagnostic Method of High-Current Converter Using Wavelet Neural Network Based on Improved Adaptive Genetic Algorithms

Abstract:

A novel method for fault Diagnosis of High-current converter, which is constructed on the basis of Wavelet Neural Network and Improved Adaptive Genetic Algorithms (IAGA), is presented here. In the proposed method, IAGA is employed to optimize the structure and the parameters of WNN and enhance the complexity, convergence and generalization ability of the network. By training and testing under MATLAB/SIMULINK, it is clearly shown that WNN based on IAGA performs better than WNN based on BP（Back-Propagation Neural Network）as well as linear adaptive GA (LAGA).

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 466-467)

Pages:

1150-1155

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.466-467.1150

Citation:

Cite this paper

Online since:

February 2012

Authors:

Te Fang Chen, Qiang Fu, Jiao Jiao Zhu

Keywords:

BP, Fault Diagnosis, High-Current Converter, Improved Adaptive GA, LAGA, WNN

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Tengfang Chen, Electric traction system and fault diagnosis technology, Changsha, China, 2009. 9.

Google Scholar

[2] Changhua Hu, Guohua Li, Tao Li, Zhijie Zhou, Wavelet Analysis and Design Based on MATLAB 6. X, Xi'an, China, 2004. 1.

Google Scholar

[3] J. Zhang, G.G. Walter, W.N. Wayne Lee. Wavelet Neural Networks for Function Learning [J]. IEEE Transaction on Signal Processing, Vol. 43, No. 6. pp.1485-1497. (1995).

DOI: 10.1109/78.388860

Google Scholar

[4] Pati, Y.C., and Krishnaprasad, P.S., Analysis and synthesis of feed forward neural networks using discrete affine wavelet transformations [J], IEEE Trans. Neural Networks., 1993, 4, (1), p.73–85.

DOI: 10.1109/72.182697

Google Scholar

[5] T.Q.D. Khao L.M. Phuong, P.T.T. Binh, N.T.H. Lien. Application of Wavelet and Neural network to Long-Term Load Forecasting [J]. International Conference on Power System technology, POWERCON 2004, Singapore, pp.840-844. (2004).

DOI: 10.1109/icpst.2004.1460110

Google Scholar

[6] Zhang Bangli, Li Yingguo, Cao Changxiu. Construction of wavelet neural networks and its robustness analysis of learning algorithm [J].

Google Scholar

[7] Back T. Evolution strategies, an alternative evolutionary algorithm [A]. Artificial evolution: European conference selected papers [C]. Springer , Lecture Notes in Computer Science , 1995. 1～20.

Google Scholar

[8] Goldberg D E. Genetic Algorithms in Search, Optimization and machine learning [M]. New York : Addison - Wesley , 1989. 1～83.

Google Scholar