Aiming at the issue of fault prediction technique of power electronic circuits, a method based on characteristic parameter data and Particle Swarm Optimization RBF(Radial Basis Function) Neural Network for the fault prediction of power electronic circuits was proposed. Taking the Buck converter circuit as an example,the fault prediction of power electronic circuits was achieved. Firstly,the output voltage was selected as monitoring signal, then the average voltage and ripple voltage were extracted as characteristic parameters. Lastly Particle Swarm Optimization RBF Neural Network was used to predict the fault. The experimental results show that the Particle Swarm Optimization RBF Neural Network is more accurate in predicting than the only RBF Neural Network.The new method can trace the characteristic parameters’ trend and can be effectively applied in fault prediction of power electronic circuits.
Performance-aware placement and chaining scheme for virtualized network functions: a particle swarm optimization approach
