在最小二乘方法(RLS,recursive least square)的基础上,提出利用格型递归最小二乘(LRLS,lattice recursiveleast square)算法对AR模型参数进行自适应估计。该算法为模块式的多极格型结构,降低了一般RLS算法的计算复杂度。利用实测的动...在最小二乘方法(RLS,recursive least square)的基础上,提出利用格型递归最小二乘(LRLS,lattice recursiveleast square)算法对AR模型参数进行自适应估计。该算法为模块式的多极格型结构,降低了一般RLS算法的计算复杂度。利用实测的动态数据结合AIC准则建立自适应AR预报模型,并将该模型应用于船舶运动预报中,仿真结果表明,相对于最小二乘算法,基于LRLS算法的AR预报模型可有效提高船舶运动预报精度。展开更多
Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive act...Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.展开更多
为取得更有效的预报效果,在深入分析传统LMS(Least mean square)算法的基础上,提出利用仿射投影算法对AR模型参数进行自适应估计,利用实测的动态数据结合AIC(Akaike information criterion)准则建立自适应AR(Autoregressive)预报模型,...为取得更有效的预报效果,在深入分析传统LMS(Least mean square)算法的基础上,提出利用仿射投影算法对AR模型参数进行自适应估计,利用实测的动态数据结合AIC(Akaike information criterion)准则建立自适应AR(Autoregressive)预报模型,并将该模型应用于船舶运动预报中,实例仿真比较分析表明:相对于LMS算法、LMS-Newton法和NLMS(归一化LMS)算法,基于仿射投影算法得到的AR预报模型,预报精度更高、预报时间更长,且在自适应AR模型参数估计中具有更快的收敛速度,能为实时在线预报提供理论依据.展开更多
文摘在最小二乘方法(RLS,recursive least square)的基础上,提出利用格型递归最小二乘(LRLS,lattice recursiveleast square)算法对AR模型参数进行自适应估计。该算法为模块式的多极格型结构,降低了一般RLS算法的计算复杂度。利用实测的动态数据结合AIC准则建立自适应AR预报模型,并将该模型应用于船舶运动预报中,仿真结果表明,相对于最小二乘算法,基于LRLS算法的AR预报模型可有效提高船舶运动预报精度。
文摘Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.
文摘为取得更有效的预报效果,在深入分析传统LMS(Least mean square)算法的基础上,提出利用仿射投影算法对AR模型参数进行自适应估计,利用实测的动态数据结合AIC(Akaike information criterion)准则建立自适应AR(Autoregressive)预报模型,并将该模型应用于船舶运动预报中,实例仿真比较分析表明:相对于LMS算法、LMS-Newton法和NLMS(归一化LMS)算法,基于仿射投影算法得到的AR预报模型,预报精度更高、预报时间更长,且在自适应AR模型参数估计中具有更快的收敛速度,能为实时在线预报提供理论依据.
