Research on fault diagnosis of magnetic resonance imaging equipment based on PSO and BP neural network
Aiming at the low accuracy and efficiency in fault diagnosis of magnetic resonance imaging equipment,a fault diagnosis method based on particle swarm optimization algorithm and back propagation neural network combined with Dempster-Schafer evidence theory is proposed.This method optimizes the parameters of back propagation neural network by particle swarm algorithm and fuses multi-sensor data by combining Dempster-Schafer evidence theory.Experimental results show that the average detection accuracy of the proposed model for 10 types of faults is 96.2%,the single sample detection time is 17.5 ms,and the accuracy rate reaches 100%in the detection of 10 types of faults.
PSO algorithmBP neural networkmagnetic resonance imaging equipmentfault diagnosisDemp-ster-Shafer evidence theory
万方数据
维普
