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| 1 | 基于改进GA-BP网络的系泊缆力预测建模与仿真显示文摘针对大型开敞式码头系靠泊安全保障和预警控制需求,研究了一类基于遗传算法和BP网络的系泊船舶缆力预测模型。考虑影响系泊缆力的环境动力因素,使用权值统计法确定了预测模型的结构;利用个体父代信息和当代个体的局部梯度信息对预测模型的学习方法进行了改进;基于改进的预测模型,提出了大型开敞式码头系泊船舶缆力预测方法。仿真结果表明:改进后的系泊船舶缆力预测模型在进化代数、最大适应度和预测精度等方面的性能均有所提高,且预测误差均值低于10%,满足实际需求。 | 李世峰 邱占芝 | 2017 | 系统仿真学报2017，29，7： | 2 |
| 2 | Influence of Regular Wave and Ship Characteristics on Mooring Force Prediction by Data-Driven Model显示文摘The study of mooring forces is an important issue in marine engineering and offshore structures.Although being widely applied in mooring system,numerical simulations suffer from difficulties in their multivariate and nonlinear modeling.Data-driven model is employed in this paper to predict the mooring forces in different lines,which is a new attempt to study the mooring forces.The height and period of regular wave,length of berth,ship load,draft and rolling period are considered as potential influencing factors.Input variables are determined using mutual information(MI)and principal component analysis(PCA),and imported to an artificial neural network(NN)model for prediction.With study case of 200 and 300 thousand tons ships experimental data obtained in Dalian University of Technology,MI is found to be more appropriate to provide effective input variables than PCA.Although the three factors regarding ship characteristics are highly correlated,it is recommended to input all of them to the NN model.The accuracy of predicting aft spring line force attains as high as 91.2%.The present paper demonstrates the feasibility of MI-NN model in mapping the mooring forces and their influencing factors. | LIU Bi-jin CHEN Xiao-yun ZHANG You-quan XIE Jing CHANG Jiang | 2020 | China Ocean Engineering2020，34，4： | 0 |
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