Research Of Ship Rolling Motion Prediction Based On Neural Network

Ships in the waves will have complex movements of six degrees of freedom, which has strong randomness and the non-linearity. So the prediction of ship sway motion for ship navigation has an important significance. Currently, due to unclear understanding of the ship motion mechanism in waves, the prediction of time domain for ship sway motions is limited to10seconds, hereby restricts its application. This paper based on the ship roll motion prediction aims to find out the nonlinearity dynamics held in the ship roll motion and confirm the characteristics of predictability and chaos, in order to improve the accuracy of forecast and prolong the time length of prediction, which can be used in the practice of ship navigation. The main research work is as follows:1. This paper proposed that the ship roll motion of time series is a chaotic system through the analysis of its characteristics of predictability and chaos. The feedforward neural networks and recurrent neural networks prediction model based on chaos theory are introduced and it makes the network possessing a chaotic deterministic rules, which improves the accuracy of prediction for ship roll motion by the application of neural network. In addition to the above methods of the chaos added to the network, it can also use the chaotic neural network to map the chaos characteristics rooted in time series of ship rolling motion to predict. This paper introduces a chaotic diagonal recurrent neural network(CDRNN), which is used to ship rolling forecast after optimization and correction. However, compared with neural network based on phase space reconstructionprediction, the prediction accuracy of this network is low. Moreover, the current chaotic neural network for the study is not mature yet and more difficult to achieve. While the method of prediction based on phase space reconstruction is simple and feasible.2. Based on the research of diagonal recurrent neural network (DRNN), optimizing the parameters of second diagonal recurrent neural networks(SDRNN). The models of DRNN and SDRNN based on phase space reconstruction are proposed to forecast the ship rolling motion and results are better than non-reconstruction network. The optimized SDRNN is better than SDRNN in ship rolling motion prediction.3. Because the training for recurrent networks complex and issue of memories fading exists, a new method is going to be brought forward that is used echo state networks(ESN) to predict the ship rolling motion. This method can effectively forecast time of17seconds more and accuracy of prediction is4-fold higher than other existing means.4. As the highly complex nature of ships sailing in the waves, the traditional single prediction method has a problem of poor self-adapt. One way by using redundant of non-negative constraints and co-integration theory for screening the model is proposed to carry on a combination forecasts, and giving a single model of the screening process to avoid the current selection of model by personal experience. It achieves the purpose of improving the prediction accuracy. At last, the performance index and evaluation criteria are given for ship sway motion prediction. And the methods in this article are used to evaluate by quantitative contrast. The validity of proposed method in the paper is confirmed.