Research of Ship Autopilot Rudder Based on Deep Belief Network

Abstract

1 School of Mathematics and Computer Science, Jianghan University, Hubei, China 2 Merchant Marine College, Shanghai Maritime University, Shanghai, China Abstract. In order to improve the control precision of the existing ship autopilot and improve the adaptive capability of the autopilot, an autopilot control algorithm based on the deep confidence network (DBN) is proposed. First of all, using the contrast divergence algorithm and the data recorded in the examination system of the Shanghai Maritime University, the constrained Boltzmann machines (RBMs) that make up each DBN are pre-trained in turn, and the results are used as the depth nerve Network weight of the initial value. On this basis, the back propagation algorithm is used to fine-tune the multi-layer depth structure. The simulation results show that the simulated sailing error between this method and the master captain is only 5.2%.