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一种面向航空母舰甲板运动状态预估的鲁棒学习模型

王可 徐明亮 李亚飞 姜晓恒 鲁爱国 李鉴

王可, 徐明亮, 李亚飞, 姜晓恒, 鲁爱国, 李鉴. 一种面向航空母舰甲板运动状态预估的鲁棒学习模型. 自动化学报, 2021, 48(x): 1−9 doi: 10.16383/j.aas.c210064
引用本文: 王可, 徐明亮, 李亚飞, 姜晓恒, 鲁爱国, 李鉴. 一种面向航空母舰甲板运动状态预估的鲁棒学习模型. 自动化学报, 2021, 48(x): 1−9 doi: 10.16383/j.aas.c210064
Wang Ke, Xu Ming-Liang, Li Ya-Fei, Jiang Xiao-Heng, Lu Ai-Guo, Li Jian. A robust learning model for deck motion prediction of aircraft carrier. Acta Automatica Sinica, 2021, 48(x): 1−9 doi: 10.16383/j.aas.c210064
Citation: Wang Ke, Xu Ming-Liang, Li Ya-Fei, Jiang Xiao-Heng, Lu Ai-Guo, Li Jian. A robust learning model for deck motion prediction of aircraft carrier. Acta Automatica Sinica, 2021, 48(x): 1−9 doi: 10.16383/j.aas.c210064

一种面向航空母舰甲板运动状态预估的鲁棒学习模型

doi: 10.16383/j.aas.c210064
基金项目: 国家自然科学基金 (62036010), 中国博士后科学基金 (2020M682348), 河南省高等学校重点科研项目计划 (21A520002), 国家自然科学基金 (61972362), 河南省自然科学基金 (202300410378), 国家自然科学基金 (61802351)资助
详细信息
    作者简介:

    王可:郑州大学计算机与人工智能学院讲师. 研究方向为基于计算智能的优化与学习

    徐明亮:郑州大学计算机与人工智能学院教授. 主要研究方向为计算机图形学、人工智能. 本文通讯作者. E-mail: iexumingliang@zzu.edu.cn

    李亚飞:郑州大学计算机与人工智能学院教授. 主要研究方向为群体智能和机器学习

    姜晓恒:郑州大学计算机与人工智能学院副教授. 主要研究方向为深度学习、机器视觉

    鲁爱国:武汉数字工程研究所(709所)研究员. 主要研究方向为信息系统与软件、人机交互

    李鉴:武汉数字工程研究所(709所)研究员. 主要研究方向为信息系统与软件

A Robust Learning Model for Deck Motion Prediction of Aircraft Carrier

Funds: Supported by National Natural Science Foundation of P. R. China (62036010), China Postdoctoral Science Foundation (2020M682348), Key Research Foundation of Henan Higher Education Institutions (21A52002), National Natural Science Foundation of P. R. China (61972362), Natural Science Foundation of Henan Province (202300410378), National Natural Science Foundation of P. R. China (61802351)
More Information
    Author Bio:

    WANG Ke Lecturer at School of Computer and Artificial Intelligence, Zhengzhou University. His research interests include computational intelligence based optimization and learning

    XU Ming-Liang Professor at School of Computer and Artificial Intelligence, Zhengzhou University. His research interests include computer graphics and artificial intelligence. Corresponding author of this paper

    LI Ya-Fei Professor at School of Computer and Artificial Intelligence, Zhengzhou University. His research interests include swarm intelligence and machine learning

    JIANG Xiao-Heng Associate professor at School of Computer and Artificial Intelligence, Zhengzhou University. His research interests include deep learning and computer vision

    LU Ai-Guo Professor at Wuhan Digital Engineering Institute (No. 709 Research Institute). His research interests include information system and software, human-computer interaction

    LI Jian Professor at Wuhan Digital Engineering Institute (No. 709 Research Institute). His research interests include information System and Software

  • 摘要: 航母甲板在风、浪、流等因素影响下做六自由度不规则运动, 影响舰载机着舰精度. 航母甲板运动预估与补偿是自动着舰系统的重要功能之一, 也是提高舰载机着舰安全性与成功率的关键技术之一. 本文提出一种面向甲板运动预估的鲁棒学习模型, 通过基本构建单元自适应演化出复杂学习系统. 构建单元的训练采用非梯度的伪逆学习策略, 提高了训练效率, 简化了学习控制超参数调优;构建单元的架构设计采用数据驱动的策略, 简化了架构超参数调优;采用图拉普拉斯正则化方法提高了模型的鲁棒性. 通过某型航母在中等海况条件下以典型航速巡航时的仿真实验, 验证了所提方法在甲板纵摇、横摇以及垂荡运动预估问题中的有效性及鲁棒性.
  • 图  1  舰船平移运动及摇荡运动

    Fig.  1  The translational motion and swaying motion of a ship

    图  2  多网络集成学习系统架构

    Fig.  2  The architecture of the ensemble learning system with multiple sub-models

    图  3  不同信噪比下的甲板纵摇预估结果

    Fig.  3  The prediction result of deck pitch with different SNR

    图  5  不同信噪比下的甲板横摇预估结果

    Fig.  5  The prediction result of deck roll with different SNR

    图  7  不同信噪比下的甲板垂荡预估结果

    Fig.  7  The prediction result of deck heave with different SNR

    图  4  PILAE与PILAE-Lap的甲板纵摇预估结果对比

    Fig.  4  he deck pitch prediction result comparison between PILAE and PILAE-Lap

    图  6  PILAE 与 PILAE-Lap 的甲板横摇预估结果对比

    Fig.  6  The deck roll prediction result comparison between PILAE and PILAE-Lap

    图  8  PILAE与PILAE-Lap的甲板垂荡预估结果对比

    Fig.  8  The deck heave prediction result comparison between PILAE and PILAE-Lap

    图  9  本文所提方法与其它方法的训练耗时对比

    Fig.  9  Training time comparison between our method and others

    图  10  本文方法生成的网络架构及运动预估性能

    Fig.  10  The network architectures generated by our proposed method and its prediction performance

    图  11  预估性能与子模型个数的关系

    Fig.  11  The prediction performance with different number of sub-model

    表  1  本文所提方法与其它方法的均方误差对比

    Table  1  Comparison of prediction MSE between our proposed method with others

    MethodsPitchRollHeave
    BPNN0.02120.01650.0754
    ELM0.01980.11650.0765
    KELM-PSO0.01240.01370.0560
    Kalman filter0.02240.57370.0261
    Autoregression0.00660.01680.0208
    Ours0.00150.02540.0029
    下载: 导出CSV
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