WCSE 2019 SUMMER ISBN: 978-981-14-1684-2
DOI: 10.18178/wcse.2019.06.139

Research of Ship Autopilot Rudder Based on Deep Belief Network

Li Shaowei, Wang Shengzheng

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%.

Index Terms— Autopilot Rudder, Deep Neural Networks, CD Algorithm, RBM, BP, Training

Li Shaowei
School of Mathematics and Computer Science, Jianghan University, CHINA
Wang Shengzheng
Merchant Marine College, Shanghai Maritime University, CHINA


Cite: Li Shaowei, Wang Shengzheng, "Research of Ship Autopilot Rudder Based on Deep Belief Network," Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering, pp. 928-933, Hong Kong, 15-17 June, 2019.