Ship motion,with six degrees of freedom,is a complex stochastic process.Sea wind and waves are the primary influencing factors.Prediction of ship motion is significant for ship navigation.To eliminate errors,a path pr...Ship motion,with six degrees of freedom,is a complex stochastic process.Sea wind and waves are the primary influencing factors.Prediction of ship motion is significant for ship navigation.To eliminate errors,a path prediction model incorporating ship pitching was developed using the Gray topological method,after analyzing ship pitching motions.With the help of simple introduction to Gray system theory,we selected a group of threshold values.Based on an analysis of ship pitch angle sequences over 40 second intervals,a Grey metabolism GM(1,1) model was established according to the time-series which every threshold corresponded to.Forecasting future ship motion with the GM(1,1) model allowed drawing of the forecast curve with effective forecasting points.The precision of the test results show that the model is accurate,and the forecast results are reliable.展开更多
The transformation of parallel translation can improve the smoothness of discrete series sometimes. In this paper, for ship pitch, a method to modify the system error is proposed via the transformation of parallel tra...The transformation of parallel translation can improve the smoothness of discrete series sometimes. In this paper, for ship pitch, a method to modify the system error is proposed via the transformation of parallel translation, which can give the optimize parameters using the Method of Minimum Squares. The series in the method can fit white exponential law better, and then be applied in GM (1,1) very well. The numerical experiments imply that the method is practical, which make the ship pitch system model more accurate than GM ( 1,1 ).展开更多
GM(1, 1) is generally used in Grey System Theory which constructs an Ordinary Differential Equation for given se-ries. It is effective for monotone series, and its simulating effect is good and error is small. However...GM(1, 1) is generally used in Grey System Theory which constructs an Ordinary Differential Equation for given se-ries. It is effective for monotone series, and its simulating effect is good and error is small. However, If the series dosen’ t havea property of monotone, the simulating effect of GM(1,1) is not fine, and its error gets bigger. In this paper, we use GM(2,1) to handle the oxcillation series, which uses the Method of Minimum Squares in determining the uncertain parameters.展开更多
文摘Ship motion,with six degrees of freedom,is a complex stochastic process.Sea wind and waves are the primary influencing factors.Prediction of ship motion is significant for ship navigation.To eliminate errors,a path prediction model incorporating ship pitching was developed using the Gray topological method,after analyzing ship pitching motions.With the help of simple introduction to Gray system theory,we selected a group of threshold values.Based on an analysis of ship pitch angle sequences over 40 second intervals,a Grey metabolism GM(1,1) model was established according to the time-series which every threshold corresponded to.Forecasting future ship motion with the GM(1,1) model allowed drawing of the forecast curve with effective forecasting points.The precision of the test results show that the model is accurate,and the forecast results are reliable.
文摘The transformation of parallel translation can improve the smoothness of discrete series sometimes. In this paper, for ship pitch, a method to modify the system error is proposed via the transformation of parallel translation, which can give the optimize parameters using the Method of Minimum Squares. The series in the method can fit white exponential law better, and then be applied in GM (1,1) very well. The numerical experiments imply that the method is practical, which make the ship pitch system model more accurate than GM ( 1,1 ).
文摘GM(1, 1) is generally used in Grey System Theory which constructs an Ordinary Differential Equation for given se-ries. It is effective for monotone series, and its simulating effect is good and error is small. However, If the series dosen’ t havea property of monotone, the simulating effect of GM(1,1) is not fine, and its error gets bigger. In this paper, we use GM(2,1) to handle the oxcillation series, which uses the Method of Minimum Squares in determining the uncertain parameters.
