Prediction of Discharge Current Using Reservoir Computing in Electric Propulsion

抄録

Artificial neural networks are used to predict discharge current, a series of the performance of Hall thrusters. To reduce the calculation cost of predictions, we used an Echo State Network (ESN), a model of reservoir computing, for the prediction of the one-step-ahead and ten-step-ahead discharge current dependency on magnetic field strength using operation parameters and plume images as an input data set. The mean absolute percentage error was 1.70% and its calculation time was about 1.0 second, which was about 1/20,000 in comparison with our previous study using Feedforward Neural Network and Convolutional Neural Network.