楼梦瑶, 王旭阳, 陈瑞, 等. 基于NARX神经网络的船舶升沉运动实时预测方法[J]. 中国舰船研究, 2020, 15(1): 48–55, 67. doi: 10.19693/j.issn.1673-3185.01717
引用本文: 楼梦瑶, 王旭阳, 陈瑞, 等. 基于NARX神经网络的船舶升沉运动实时预测方法[J]. 中国舰船研究, 2020, 15(1): 48–55, 67. doi: 10.19693/j.issn.1673-3185.01717
shu
LOU M Y, WANG X Y, CHEN R, et al. A real-time prediction method for ship heave motion using NARX neural network[J]. Chinese Journal of Ship Research, 2020, 15(1): 48–55, 67. doi: 10.19693/j.issn.1673-3185.01717
Citation: LOU M Y, WANG X Y, CHEN R, et al. A real-time prediction method for ship heave motion using NARX neural network[J]. Chinese Journal of Ship Research, 2020, 15(1): 48–55, 67. doi: 10.19693/j.issn.1673-3185.01717
shu

基于NARX神经网络的船舶升沉运动实时预测方法

A real-time prediction method for ship heave motion using NARX neural network

    摘要
  • 摘要:
      目的  对船舶升沉运动进行预测有助于增强升沉补偿器的补偿效果，减少海浪对作业设备的干扰。为提高升沉预测模型的精度和稳定性，提出一种船舶升沉运动实时预测方法。
      方法  基于带外源输入的非线性自回归（NARX）神经网络建立单海况预测模型，利用船舶系统仿真器获取母船升沉运动仿真数据，将NARX模型与卡尔曼（Kalman）模型、普通反向传播（BP）模型的预测结果进行对比。在此基础上，对单海况预测模型进行改进，建立多海况预测模型。
      结果  多海况预测模型预测精度较高，且稳定性优于单海况模型，在2~5级海况下的最大预测误差均小于10−4量级。
      结论  仿真结果表明，NARX神经网络对复杂海浪环境具有良好的适应性，它的预测速度和精度均优于BP神经网络和传统滤波方法，在高海况下仍可保持高预测精度。

     

    Abstract:
      Objectives  Predicting heave motion is helpful for improving the performance of the heave compensator and reducing the disturbance of waves on operating equipment. To improve the accuracy and stability of the heave prediction model, a real-time prediction method for ship heave motion is proposed in this paper.
      Methods  Based on the Nonlinear Autoregressive with eXogeneous inputs (NARX) neural network, a single sea-state prediction model is established. The simulated heave motion of the vessel is obtained using the Marine Systems Simulator software tool to verify the model. The prediction model based on NARX is compared with prediction models based on Kalman and BP. On this basis, a multi sea-state prediction model is established by improving the single sea-state model.
      Results  The prediction accuracy requirements of the multi sea-state prediction model are satisfied, and its stability is better than the single sea-state model, with a maximum prediction error of less than 10−4 magnitude in the range of sea state from 2 to 5.
      Conclusions  The simulation results verify the good adaptability of the NARX neural network to the complex wave environment. Its prediction speed and accuracy are higher than the common back-propagation neural network and the traditional filtering method. It still maintains high prediction accuracy under high sea state.

     

    • HTML全文
    • 参考文献(14)
    • 相关文章
    • 施引文献
  • 资源附件(0)

/

返回文章
返回