Studies Of Wavelet Analysis And Chaotic Time Series In Prediction Of Ship Motions

The sea wave and ocean interfere with the ship current during its navigation process, the ship produced a complex coupled six degrees of freedom of movement, especially in the bad sea condition, its motion is a non-linear random process. And in the voyage of the same sea area at different times or in different sea areas, its Statistical properties are different. Therefore, the statistical properties of ship motion has a certain non-stationary.Short term motion prediction have a great significance for the operations of offshore platform and ship at sea. According to historical movement data, predict the future movement of a extreme short time, then take the appropriate measures, especially for case of motion compensation or have a critical motion restrictions during operating.In recent years, the study of extreme short term prediction of ship motions is becoming more and more. Among of those, Time Series Analysis Method has got the most attemion. Such algorithm only need to seek the law of the historical data, then we can get the predicted value. This algorithm computation load small, and easy to implement.The main jobs of this thesis are stated as follows:1. First, describeing the background of this study, recalling the development process and research status of the extreme short term prediction of ship motion. Compared a variety of prediction algorithms, and summarize their advantages and disadvantages.2. First, describing the traditional time series algorithms of extreme short term prediction of ship motion based on AR model, verify the feasibility of the algorithm. However, the traditional time series prediction algorithm is less effective.3. Using of multi-scale wavelet theory, non-stationary time series is decomposed into several layers of stationary time series approximately; then use time series model to predict each of layer; finally integrating each predicted layer to reconstruct the prediction of original time series. According to the original time series power spectrum analysis and the rule of the frequency band division for the wavelet multi-scale analysis, can select the best decomposed layer. According to different characteristics of wavelet basis functions, select the appropriate wavelet basis function.4. Analysis the effects of the empirical mode decomposition method for non-stationary signal processing, according to the non-stationary time series of the ship motion, select the relevant parameters in the algorithm. Use the empirical mode decomposition algorithm to improvement the traditional time series prediction algorithm and multi-resolution wavelet time series prediction algorithm.5. By the chaos identify of the ship motion, select the embedding dimension and time delay. Using the recursive least squares algorithm to identify the Volterra series prediction model parameters. Compare the simulation results of this chaotic time series prediction algorithm with the algorithm described before.