Analog Circuit Fault Diagnosis Based on Particle Swarm Optimization Algorithm and Adaptive Learning Rate Algorithm

Bao Ru Han; Shuang Chen; Heng Yu Wu

doi:10.4028/www.scientific.net/AMM.380-384.983

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384Analog Circuit Fault Diagnosis Based on Particle...

Analog Circuit Fault Diagnosis Based on Particle Swarm Optimization Algorithm and Adaptive Learning Rate Algorithm

Abstract:

BP neural network is widely used as a multilayer feed forward neural network model. The paper puts forward a kind of adaptive learning rate algorithm and particle swarm optimization algorithm hybrid algorithm combining in order to solve the traditional BP algorithm is easy to fall into local extremum problem. So that the particle swarm optimization algorithm and adaptive learning rate algorithm are complementary. The hybrid algorithm has extensive mapping ability of neural networks and particle swarm rapid, global convergence characteristics. The simulation shows that the hybrid algorithm realizes the detection and location of analog circuit fault avoidance, has satisfied effect.

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Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

983-986

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.983

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Online since:

August 2013

Authors:

Bao Ru Han, Shuang Chen, Heng Yu Wu

Keywords:

Adaptive Learning Rate Algorithm, Analog Circuit, Fault Diagnosis

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