Prediction of ship motion attitude based on BP network
Yang Ge, Qin Ming Jie, Niu Qing Tao · 2017
When the disturbance of ship is compensated, it can't get the specific motion parameters in time. In order to solve this problem, the motion attitude needs to be predicted in advance, and the reliable data also needs to be provided for the wave compensation system. This paper introduces a method of motion attitude prediction based on BP neural network. The method solves the learning problem of hidden layer by selecting the BP neural network model, and the results show that using such method can effectively improve the prediction speed and precision of motion attitude.