Information-Applied Technology in the Model of RBF Neural Network Based on Adaptive Genetic Algorithm for Analog Circuit Fault Diagnosis

Hu Cheng Zhao; Hao Lin Cui; Zhi Bin Chen

doi:10.4028/www.scientific.net/AMM.540.456

Paper Titles

Applied-Information Technology with Trojan Horse Detection Method Based on C5.0 Decision Tree
p.439

Information-Applied Technology in Employee Involvement and Participation (EIP) Based Knowledge Map Building
p.443

Application of VOD Technology and Scheduling Algorithm in VOD Server System
p.448

Information-Applied Technology in Fault Diagnosis Model of RBF Neural Network Based on PSO Algorithm for Analog Circuit
p.452

Information-Applied Technology in the Model of RBF Neural Network Based on Adaptive Genetic Algorithm for Analog Circuit Fault Diagnosis
p.456

Research on RBFNN Modeling Based on ICA Feature Extraction
p.460

Applied Technology in Exploration of Libraries Information Literacy Network Free Course Management Service Platform-Huazhong Agricultural University Library as an Example
p.464

Research of Mask Image’s Generation Based on STL Model for Integral Stereolithography System
p.468

The Design of an Android-Based Platform for Mechanics Principle Learning and Applying
p.472

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 540Information-Applied Technology in the Model of RBF...

Information-Applied Technology in the Model of RBF Neural Network Based on Adaptive Genetic Algorithm for Analog Circuit Fault Diagnosis

Abstract:

To obtain the improvement of analog circuit fault diagnosis, a RBF diagnosis model based on an Adaptive Genetic Algorithm (AGA) is proposed. First an adaptive mechanism about crossover and mutation probability is introduced into the traditional genetic algorithm, and then AGA algorithm is used to optimize the network parameters such as center, width and connection weight. The experiment simulation indicates that the proposed model has exact diagnosis characteristic.

You might also be interested in these eBooks

Advanced Research on Mechanics, Manufacturing Engineering and Applied Technology II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 540)

Pages:

456-459

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.540.456

Citation:

Cite this paper

Online since:

April 2014

Authors:

Hu Cheng Zhao*, Hao Lin Cui, Zhi Bin Chen

Keywords:

Analog Circuit, Crossover Probability, Fault Diagnosis, Genetic Algorithm (GA), Radial Basis Function (RBF)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] S. Chert, S. A. Billings, P. M. Grant. Recursive hybrid algorithm for non-linear system identification using radial basis function networks. International Journal of Control, 55(5), (1992), 1051-1070.

DOI: 10.1080/00207179208934272

Google Scholar

[2] S. Chert, P. M. Crant, C. F. N. Cown. Orthogonal least square algorithm for radial basis function networks. IEEE Transaction on Neural Networks, 2(2), (1991), 302-309.

DOI: 10.1109/72.80341

Google Scholar

[3] J. H. Holland. Adaptation in natural artificial systems. MIT Press, (1975).

Google Scholar

[4] H. Y. Kuang, J. Jin, Y. Su. Improving crossover and mutation for adaptive genetic algorithm. Computer Engineering and Applications, 12, (2006), 93-96.

Google Scholar

[5] M. Srinvas, L. M. Patnaik. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 24(4), (1994), 656-667.

DOI: 10.1109/21.286385

Google Scholar

[6] A. Fanni, A. Giua, M. Marchesi, et al. A neural network diagnosis approach for analog circuits. Applied Intelligence, 11(2), 1999, 169-186.

DOI: 10.1023/a:1008376430315

Google Scholar

[7] C. M. Li. Neural network approach to analog circuit fault diagnosis. Journal of Inner Mongolia Polytechnic University, 19(2), (2000), 130-133.

Google Scholar