Particle Swarm Neural Network in Application of Soft Fault Diagnosis of Analog Circuit

Bao Ru Han; Shi Xiang Liu; Li Sha Cai

doi:10.4028/www.scientific.net/AMR.694-697.1349

Paper Titles

Prediction of Nonlinear Distortion due to Acousto-Optic Interaction
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Monitoring and Diagnostics Systems Application at 35-110 kV Substations
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Safe Test System for the Turning Vehicles ESP Control Performances on the Lateral Restricted Vehicle System
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The Vehicle Stability Test System Based on Active Brake Control for Overturning Prevention
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Particle Swarm Neural Network in Application of Soft Fault Diagnosis of Analog Circuit
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Study on the Application of FPGA in Magnetic Flux Leakage Acquisition System
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Design of Multi-Channel Lithium-Ion Battery Test System
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Modeling Dynamic Laser Speckle Patterns of Plant Leaves
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 694-697Particle Swarm Neural Network in Application of...

Particle Swarm Neural Network in Application of Soft Fault Diagnosis of Analog Circuit

Abstract:

In order to diagnose single soft fault in analog circuit, the particle swarm neural network was applied to fault diagnosis of analog circuit. The particle swarm neural network training process was divided into two steps. Firstly, BP network weights and threshold values as the position vector of the particle, used PSO algorithm searches for a near-optimal position vector as BP neural network initial weight values and thresholds. Secondly, used the BP algorithm to further optimization based on the initial weights and thresholds, got the optimal network weights and threshold value. The training method can improve the convergence accuracy and learning speed of the network training. The simulation results show that this diagnostic method can effectively achieve the accurate diagnosis of analog circuit soft fault.

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Periodical:

Advanced Materials Research (Volumes 694-697)

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1349-1353

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.694-697.1349

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Online since:

May 2013

Authors:

Bao Ru Han, Shi Xiang Liu, Li Sha Cai

Keywords:

Analog Circuit, Particle Swarm Neural Network, Soft Fault

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