Accelerating RNN Controllers with Parallel Computing and Weight Dropout Techniques

Training recurrent neural network controllers in closed-loop control systems with combined Levenberg-Marquardt and Forward Accumulation Through Time algorithm advances the research in a grid-connected converter for solar integration to a power system. However, an effective training algorithm is needed for a large number of trajectories with a high sampling frequency. Thus, we propose a new effective training mechanism based on parallel computing and weight dropout techniques for recurrent neural network controllers in this paper. Experimental results on both the Amazon Web Services (AWS) cloud and the Graphical Processing Unit (GPU) show that our proposed training mechanism runs at a more promising acceleration rate than the existing algorithms.