为实现船舶操纵性的在线预报及自适应运动控制,针对Nomoto二阶非线性运动模型参数辨识问题,将最小二乘支持向量机(least squares support vector machines,LSSVM)与多新息方法相结合,提出一种新的多新息在线LSSVM辨识建模方法。试验结...为实现船舶操纵性的在线预报及自适应运动控制,针对Nomoto二阶非线性运动模型参数辨识问题,将最小二乘支持向量机(least squares support vector machines,LSSVM)与多新息方法相结合,提出一种新的多新息在线LSSVM辨识建模方法。试验结果表明,使用所提出的算法辨识的模型进行预报的拟合误差可达到4.76%以下,能准确拟合船舶操纵运动模型。展开更多
By analyzing the data of longitudinal speed, transverse speed and rudder angle etc. in the simulated 10°/10°zigzag test, the hydrodynamic derivatives in the Abkowitz model for ship manoeuvring motion are ide...By analyzing the data of longitudinal speed, transverse speed and rudder angle etc. in the simulated 10°/10°zigzag test, the hydrodynamic derivatives in the Abkowitz model for ship manoeuvring motion are identified by using e-Support Vector Regression (ε -SVR). To damp the extent of parameter drift, a series of random numbers are added into the training samples to reconstruct the training samples. The identification results of the hydrodynamic derivatives are compared with the Planar Motion Mechanism (PMM) test results to verify the identification method. By using the identified Abkowitz model, 20°/20° zigzag test is numerically simulated. The simulated results are compared with those obtained by using the Abkowitz model where the hydrodynamic derivatives are obtained from PMM tests. The agreement is satisfactory, which shows that the regressive Abkowitz model has a good generalization performance.展开更多
In this paper, Neural Networks (NNs) are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverabil...In this paper, Neural Networks (NNs) are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverability indices and linear non-dimensional hydrodynamic derivatives in the models are identified by using two-layer feed forward NNs. The stability of parametric estimation is confirmed. Then, the ship maneuvering motion is predicted based on the obtained models. A comparison between the predicted results and the model test results demonstrates the validity of the proposed modeling method.展开更多
The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while...The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System(AIS) data available. First, we subdivide three typical cargo ship transportation networks(i.e., oil tanker, container ship and bulk carrier) from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks(i.e., degree-based attack, betweenness-based attack and flux-based attack) to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1) compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2) different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3) small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.Abstract: The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coa展开更多
Based on support vector machines, three modeling methods, i.e., white-box modeling, grey-box modeling and black-box modeling of ship manoeuvring motion in 4 degrees of freedom are investigated. With the whole-ship mat...Based on support vector machines, three modeling methods, i.e., white-box modeling, grey-box modeling and black-box modeling of ship manoeuvring motion in 4 degrees of freedom are investigated. With the whole-ship mathematical model for ship manoeuvring motion, in which the hydrodynamic coefficients are obtained from roll planar motion mechanism test, some zigzag tests and turning circle manoeuvres are simulated. In the white-box modeling and grey-box modeling, the training data taken every 5 s from the simulated 20°/20° zigzag test are used, while in the black-box modeling, the training data taken every 5 s from the simulated 15°/15°, 20°/20° zigzag tests and 15°, 25° turning manoeuvres are used; and the trained support vector machines are used to predict the whole 20°/20° zigzag test. Comparisons between the simulated and predicted 20°/20° zigzag tests show good predictive ability of the proposed methods. Besides, all mathematical models obtained by the proposed modeling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the proposed modeling methods are analyzed and compared with each other in aspects of application conditions, prediction accuracy and computation speed. The appropriate modeling method can be chosen according to the intended use of the mathematical models and the available data needed for system identification.展开更多
文摘为实现船舶操纵性的在线预报及自适应运动控制,针对Nomoto二阶非线性运动模型参数辨识问题,将最小二乘支持向量机(least squares support vector machines,LSSVM)与多新息方法相结合,提出一种新的多新息在线LSSVM辨识建模方法。试验结果表明,使用所提出的算法辨识的模型进行预报的拟合误差可达到4.76%以下,能准确拟合船舶操纵运动模型。
基金Supported by the Special Research Fund for the Doctoral Program of Higher Education (Grant No.20050248037)the National Natural Science Foundation of China (Grant No.10572094)
基金supported by the National Natural Science Foundation of China (Grant Nos.50979060,51079031)the Foundation of National Science and Technology Key Laboratory of Hydrodynamics (Grant No.9140C2201091001)
文摘By analyzing the data of longitudinal speed, transverse speed and rudder angle etc. in the simulated 10°/10°zigzag test, the hydrodynamic derivatives in the Abkowitz model for ship manoeuvring motion are identified by using e-Support Vector Regression (ε -SVR). To damp the extent of parameter drift, a series of random numbers are added into the training samples to reconstruct the training samples. The identification results of the hydrodynamic derivatives are compared with the Planar Motion Mechanism (PMM) test results to verify the identification method. By using the identified Abkowitz model, 20°/20° zigzag test is numerically simulated. The simulated results are compared with those obtained by using the Abkowitz model where the hydrodynamic derivatives are obtained from PMM tests. The agreement is satisfactory, which shows that the regressive Abkowitz model has a good generalization performance.
基金Partially Supported by the Special Item for the Fujian Provincial Department of Ocean and Fisheries(No.MHGX-16)the Special Item for Universities in Fujian Province by the Education Department(No.JK15003)
文摘In this paper, Neural Networks (NNs) are used in the modeling of ship maneuvering motion. A nonlinear response model and a linear hydrodynamic model of ship maneuvering motion are also investigated. The maneuverability indices and linear non-dimensional hydrodynamic derivatives in the models are identified by using two-layer feed forward NNs. The stability of parametric estimation is confirmed. Then, the ship maneuvering motion is predicted based on the obtained models. A comparison between the predicted results and the model test results demonstrates the validity of the proposed modeling method.
基金Key Project of the Chinese Academy of Sciences,No.ZDRW-ZS-2016-6-3National Natural Science Foundation of China,No.41501490
文摘The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System(AIS) data available. First, we subdivide three typical cargo ship transportation networks(i.e., oil tanker, container ship and bulk carrier) from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks(i.e., degree-based attack, betweenness-based attack and flux-based attack) to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1) compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2) different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3) small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.Abstract: The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coa
基金financially supported by the National Natural Science Foundation of China(Grant No.51279106)the Special Research Fund for the Doctoral Program of Higher Education of China(Grant No.20110073110009)
文摘Based on support vector machines, three modeling methods, i.e., white-box modeling, grey-box modeling and black-box modeling of ship manoeuvring motion in 4 degrees of freedom are investigated. With the whole-ship mathematical model for ship manoeuvring motion, in which the hydrodynamic coefficients are obtained from roll planar motion mechanism test, some zigzag tests and turning circle manoeuvres are simulated. In the white-box modeling and grey-box modeling, the training data taken every 5 s from the simulated 20°/20° zigzag test are used, while in the black-box modeling, the training data taken every 5 s from the simulated 15°/15°, 20°/20° zigzag tests and 15°, 25° turning manoeuvres are used; and the trained support vector machines are used to predict the whole 20°/20° zigzag test. Comparisons between the simulated and predicted 20°/20° zigzag tests show good predictive ability of the proposed methods. Besides, all mathematical models obtained by the proposed modeling methods are used to predict the 10°/10° zigzag test and 35° turning circle manoeuvre, and the predicted results are compared with those of simulation tests to demonstrate the good generalization performance of the mathematical models. Finally, the proposed modeling methods are analyzed and compared with each other in aspects of application conditions, prediction accuracy and computation speed. The appropriate modeling method can be chosen according to the intended use of the mathematical models and the available data needed for system identification.
基金Supported by the National Natural Science Foundation of China(Grant Nos.50979060,51079031)the Foundation of National Science and Technology Key Laboratory of Hydrodynamics(GrantNo.9140C2201091001)