基于回声状态网络的船舶摇荡连续预报方法研究

回声状态网络（ ESNS）是一种新型递归神经网络，可通过对有限的已知样本进行训练，建立非线性模型来预报未知样本。该算法在解决非线性问题时具有一定优势。无需知道海浪的先验信息和船舶航行姿态的状态方程，仅利用实测的船舶横摇、纵摇历史数据，寻求规律即可进行实测摇荡数据的极短期预报。仿真结果表明，该算法在预报15 s以内可达到较高的预报精度，通过预报窗口的平移，可以进行连续在线预报。

Continuous forecasting of ship sway based on ESNs

Echo state networks (ESNs) is a new kind of recurrent neural networks, which is suitable for the data processing based on finite number of training samples to forecast the unknown data by a nolinear model. It has preponderance for solving the nonlinearity problems. Without prior information of sea waves and the state equations of ship motions, only using the real measured roll and pitch data, the ESNs method is applied to solve the problem of short time series forecasting. Results show that the method satisfies the need of online forecasting within 15 seconds, and continuous forecasting can be realized by sliding the window.