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

Xiao Hua Zhang; Hua Ping Li

doi:10.4028/www.scientific.net/AMM.540.452

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 540Information-Applied Technology in Fault Diagnosis...

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

Abstract:

To improve the ability of fault diagnosis for analog circuit, a RBF neural network diagnosis method trained by an improved Particle Swarm Optimization (PSO) algorithm is proposed. In order to overcome the shortcoming of the traditional BP algorithm of RBF neural network, PSO algorithm is introduced to optimize the center, width and connection weight of RBF neural network. And the mutation operator is inserted to ensure the individual in swarm out of the local optimum. The simulation shows that the proposed modeling algorithm has the better convergence and diagnosis characteristics.

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Periodical:

Applied Mechanics and Materials (Volume 540)

Pages:

452-455

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.540.452

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Online since:

April 2014

Authors:

Xiao Hua Zhang*, Hua Ping Li

Keywords:

Analog Circuit, Fault Diagnosis, Mutation Operator, Neural Network (NN), Particle Swarm Optimization (PSO), Radial Basis Function (RBF)

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* - Corresponding Author

References

[1] 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

[2] 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

[3] J. Kennedy, R. C. Eberhart. Particle swarm optimization. Proceedings of 4th IEEE International Conference on Neural Networks, Perth, Australia, Dec. (1995), 1942-(1948).

Google Scholar

[4] M. Clerc. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Congress on Evolutionary Computation, Washington, U. S., 3, (1999), 1951-(1957).

DOI: 10.1109/cec.1999.785513

Google Scholar

[5] Y. N. Liu, Y. H. Li, J. F. Liu, et al. Using RBF neural networks based on artificial fish-swarm algorithm. Journal of Northeast Dianli University, 26(4), (2006), 23-27.

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] F. X. Qin, L.J. Feng, S.T. Zhang. Ambiguous fault diagnosis of linear circuit based on BP network. Marine Electric & Electronic Technology, 30(9), (2010), 35-37.

Google Scholar