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              一種面向航空母艦甲板運動狀態預估的魯棒學習模型

              王可 徐明亮 李亞飛 姜曉恒 魯愛國 李鑒

              王可, 徐明亮, 李亞飛, 姜曉恒, 魯愛國, 李鑒. 一種面向航空母艦甲板運動狀態預估的魯棒學習模型. 自動化學報, 2021, 48(x): 1?9 doi: 10.16383/j.aas.c210664
              引用本文: 王可, 徐明亮, 李亞飛, 姜曉恒, 魯愛國, 李鑒. 一種面向航空母艦甲板運動狀態預估的魯棒學習模型. 自動化學報, 2021, 48(x): 1?9 doi: 10.16383/j.aas.c210664
              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.c210664
              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.c210664

              一種面向航空母艦甲板運動狀態預估的魯棒學習模型

              doi: 10.16383/j.aas.c210664
              基金項目: 國家自然科學基金 (62036010), 中國博士后科學基金 (2020M682348), 河南省高等學校重點科研項目計劃 (21A520002), 國家自然科學基金 (61972362), 河南省自然科學基金 (202300410378), 國家自然科學基金 (61802351)資助
              詳細信息
                作者簡介:

                王可:鄭州大學計算機與人工智能學院講師. 研究方向為基于計算智能的優化與學習

                徐明亮:鄭州大學計算機與人工智能學院教授. 主要研究方向為計算機圖形學,人工智能. 本文通訊作者. E-mail: iexumingliang@zzu.edu.cn

                李亞飛:鄭州大學計算機與人工智能學院教授. 主要研究方向為群體智能與機器學習

                姜曉恒:鄭州大學計算機與人工智能學院副教授. 主要研究方向為深度學習,機器視覺

                魯愛國:武漢數字工程研究所(709所)研究員. 主要研究方向為信息系統與軟件,人機交互

                李鑒:武漢數字工程研究所(709所)研究員. 主要研究方向為信息系統與軟件

              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 interest covers computational intelligence based optimization and learning

                XU Ming-Liang Professor at School of Computer and Artificial Intelligence, Zhengzhou University. His research interest covers 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 interest covers swarm intelligence and machine learning

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

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

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

              • 摘要: 航母甲板在風、浪、流等因素影響下做六自由度不規則運動, 影響艦載機著艦精度. 航母甲板運動預估與補償是自動著艦系統的重要功能之一, 也是提高艦載機著艦安全性與成功率的關鍵技術之一. 本文提出一種面向甲板運動預估的魯棒學習模型, 通過基本構建單元自適應演化出復雜學習系統. 構建單元的訓練采用非梯度的偽逆學習策略, 提高了訓練效率, 簡化了學習控制超參數調優;構建單元的架構設計采用數據驅動的策略, 簡化了架構超參數調優;采用圖拉普拉斯正則化方法提高了模型的魯棒性. 通過某型航母在中等海況條件下以典型航速巡航時的仿真實驗, 驗證了所提方法在甲板縱搖、橫搖以及垂蕩運動預估問題中的有效性及魯棒性.
              • 圖  1  艦船平移運動及搖蕩運動

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

                圖  2  多網絡集成學習系統架構

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

                圖  3  不同信噪比下的甲板縱搖預估結果

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

                圖  5  不同信噪比下的甲板橫搖預估結果

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

                圖  7  不同信噪比下的甲板垂蕩預估結果

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

                圖  4  PILAE與PILAE-Lap的甲板縱搖預估結果對比

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

                圖  6  PILAE 與 PILAE-Lap 的甲板橫搖預估結果對比

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

                圖  8  PILAE與PILAE-Lap的甲板垂蕩預估結果對比

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

                圖  9  本文所提方法與其它方法的訓練耗時對比

                Fig.  9  Training time comparison between our method and others

                圖  10  本文方法生成的網絡架構及運動預估性能

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

                圖  11  預估性能與子模型個數的關系

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

                表  1  本文所提方法與其它方法的均方誤差對比

                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
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