Ship Pitch Prediction Based On The Combination Forecasting Method

The movement of ship is affected by the influence of ocean waves, wind and other interactions, ships can have complex movements of six free degrees, which have the randomness and the non-linearity. So the prediction of ship motion has an important significance for the ship sailing. Extremely short time prediction of ship motion is on the basis of historical data to predict the ship motion in the future shorter time with some theory and technology. Previously time series method, periodogram, neural network method, the grey system theory and other methods of prediction of ship are applied. This paper aimed at prediction of ship pitch, combination forecasting method is studied on prediction of ship pitch. Combination prediction methods made use of effective information of the individual model prediction, based on this aspect, In this paper, several methods of individual forecasting in the prediction of ship pitch are researched. it was done aiming at some actual ship pitch angle that the prediction and simulation of ship motion. The ship pitching theoretical research can help us to understand the law of ship pitching so as to use it as ship navigation services. The research was done mainly in this paper:The basic theory of grey system modeling and data generation methods were introduced, taking into account the characteristics of trend prediction of the grey topological prediction method, combination of metabolic GM(1,1) model, a topology prediction model are set up on the ship pitch angle. According to different thresholds, setting up the metabolic GM(1,1) model group by the corresponding time series. Using effective forecasting points to draw topology prediction curve, this model can predict the possible future trends for ship pitch motion.In the process of ship pitching prediction, the emergence of point mutations affects the accuracy of modeling and forecasting, singularity detection theory of wavelet transform was applied to deal with the singularity of ship pitching angle, through modulus maxima determining the occurrence time of mutation point; a singular point of data processing methods was introduced, finally non-homogeneous GM (1,1) model was set up with the data, the model improves the forecast accuracy.The whitening equation of the traditional grey system GM (1,1) model reflects only generating data relate with themselves and their changes, in fact generating data are affected by other factors, these factors can't fully expressed by grey number. According to this issue, This article first give the analytic formula of the improved GM(1,1) grey differential equation model which obey non-pure exponential growth law, so time series response type was given; At the same time taking into account the impact of the fitting error of the initial point of the grey sequence, the initial value was changed, thus the optimized time response function was constructed, the model improves the simulation accuracy. Finally the model was applied to model for the data of ship pitching angle, numerical experiments show that this method is feasible.Take the correlation coefficient of the logarithm of forecasting value for the error standard, Weighted geometric means combination forecasting based on correlation coefficients was put forward, weighted geometric means combination forecasting is a kind of nonlinear combination prediction method. Weighted geometric means combination forecasting is a kind of nonlinear combination forecasting model. Based on correlation coefficients, a weighted geometric means combination forecasting model is proposed. Superior combination forecasting, dominant forecasting method and redundant degree are put forward. Under certain conditions the sufficient condition of existence of non-inferior combination and superior combination forecasting are discussed, redundant information is pointed out in a judging theorem. It shows that this nonlinear model is effective theoretically, at the same time, this paper verifies the validity of the model with ship pitch angle prediction through computer simulation test.