Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis Based on Particle Swarm Neural Network

Bao Ru Han; Jing Bing Li; Heng Yu Wu

doi:10.4028/www.scientific.net/AMR.712-715.1965

Paper Titles

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Improved Method for State of Charge Estimation of Lithium Iron Phosphate Power Batteries
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Ballistic Measurement and Parameter Estimation of Ballistic Missile with Early Warning Satellite
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Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis Based on Particle Swarm Neural Network
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 712-715Tolerance Analog Circuit Hard Fault and Soft Fault...

Tolerance Analog Circuit Hard Fault and Soft Fault Diagnosis Based on Particle Swarm Neural Network

Abstract:

This paper presents a tolerance analog circuit hard fault and soft fault diagnosis method based on the BP neural network and particle swarm optimization algorithm. First, select the mean square error function of BP neural network as the fitness function of the PSO algorithm. Second, change the guidance of neural network algorithms rely on gradient information to adjust the network weights and threshold methods, through the use of the characteristics of the particle swarm algorithm groups parallel search to find more appropriate network weights and threshold. Then using the adaptive learning rate and momentum BP algorithm to train the BP neural network. Finally, the network is applied to fault diagnosis of analog circuit, can quickly and effectively to the circuit fault diagnosis.

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Periodical:

Advanced Materials Research (Volumes 712-715)

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1965-1969

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https://doi.org/10.4028/www.scientific.net/AMR.712-715.1965

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Online since:

June 2013

Authors:

Bao Ru Han, Jing Bing Li*, Heng Yu Wu

Keywords:

BP Neural Network (BPNN), Fault Diagnosis, Particle Swarm Optimization Algorithm, Tolerance Analog Circuits

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