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- W2387682723 abstract "In the detection and diagnosis of faults in analog circuits,as the initial values and thresholds of the traditional BP neural network is selected at random,it is easy to fall into local minimum,and tends to the fact that there is no convergence or slow convergence,the method based on particle swarm optimization(PSO) and back-propagation(BP) neural networks is proposed.At first,optimizing the initial weights and thresholds of BP neural network,and then training and simulating with the improved BP algorithm.Comparing the simulation results with those only with a single improvement in BP algorithm,we can find that the neural network improved the convergence defect after being optimizing by genetic algorithm,and the speed and accuracy of diagnosis are also improved." @default.
- W2387682723 created "2016-06-24" @default.
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- W2387682723 date "2013-01-01" @default.
- W2387682723 modified "2023-09-23" @default.
- W2387682723 title "Optimized BP neural networks based on PSO for analog circuit fault diagnosis" @default.
- W2387682723 hasPublicationYear "2013" @default.
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