Analog Circuit Fault Diagnosis Based on Particle Swarm Neural Network Hybrid Algorithm

Bao Ru Han; Jing Bing Li

doi:10.4028/www.scientific.net/AMM.373-375.1049

Paper Titles

Research on Ontology Knowledge Based System of Meteorological and Hydrological Support
p.1027

Study on Optimization of Vehicle Routing Problem with Multiple Vehicle Type and Vehicle Access Restricted Based on Genetic Algorithm
p.1031

Probability Collectives with Collaborative Optimization (PCCO): A Novel Framework for Handling Complex Optimization Problems
p.1036

Optimization of Dynamic Job-Shop Scheduling Based on Game Theory
p.1045

Analog Circuit Fault Diagnosis Based on Particle Swarm Neural Network Hybrid Algorithm
p.1049

Parameter Optimization of SVM Based on Maximum Variance – Entropy Criterion
p.1053

Application of Variable Precision Rough Set and Integrated Neural Network to Bearing Fault Diagnosis
p.1060

Simulation and Evolutionary of Submarine in Anti-Torpedo Tactics
p.1064

Optimization Design of Helical Spring Based on Multi-Objective Genetic Algorithm
p.1068

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 373-375Analog Circuit Fault Diagnosis Based on Particle...

Analog Circuit Fault Diagnosis Based on Particle Swarm Neural Network Hybrid Algorithm

Abstract:

Base on improved particle swarm algorithm, this paper proposes a linear decreasing inertia weight particle swarm algorithm and error back propagation algorithm based on hybrid algorithm combining. The linear decreasing inertia weight particle swarm algorithm and momentum-adaptive learning rate BP algorithm interchangeably adjust the network weights, so that the two algorithms are complementary. It gives full play to the PSO's global optimization ability and the BP algorithm local search advantage, to overcome the slow convergence speed and easily falling into local weight problems. Simulation results show that this diagnostic method can be used for tolerance analog circuit fault diagnosis, with a high convergence rate and diagnostic accuracy.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 373-375)

Pages:

1049-1052

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.373-375.1049

Citation:

Cite this paper

Online since:

August 2013

Authors:

Bao Ru Han, Jing Bing Li

Keywords:

Analog Circuit, Fault Diagnosis, Hybrid Algorithm, Particle Swarm Neural Network

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Zhao Jian. Journal of Shanghai Maritime University. Vol. 24, No. 11( 2000), pp.1-7.

Google Scholar

[2] Wenjun Yao. Journal of South-Central University for Nationalities. Vol. 23, No. 3 (2004), pp.50-53.

Google Scholar

[3] Xuanfang Yang . Computer measurement and control. Vol. 14, No. 5 (2006), pp.564-566.

Google Scholar

[4] Aminian F. IEEE Transactions on Instrumentation and Measurement. Vol. 51, No. 3 (2002) pp.544-550.

Google Scholar

[5] Kennedy J. Eberhart R. IEEE on Networks. (1995), p.1942-(1948).

Google Scholar

[6] Yang Wei, Li Qiqiang. China Engineering Science. Vol. 6, No. 5 (2004), pp.87-93.

Google Scholar

[7] Song Naihua, Xing Qinghua. Computer Engineering. Vol. 32, No. 14 (2006), pp.181-183.

Google Scholar

[8] Haibing Gao. Acta Electronica Sinica. Vol. 32, No. 9 (2004), pp.1572-1574.

Google Scholar

[9] Jiang Tao, Zhang Yufang, Wang Yinhui. Computer Science. Vol. 33, No. 9(2006), pp.164-190.

Google Scholar

[10] Yasuda K, Ide A, Iwasaki N. 2003 IEEE International Conference on Systems, Man and Cybernetics. Piscataway: IEEE Service Center. (2003), pp.1554-1559.

Google Scholar