舰船运动预报在小波分析及混沌时序中的研究
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摘要
船舶在其航行过程中将受到风、浪等环境因素干扰,产生了相互耦合的复杂的六自由度运动,尤其是在恶劣海况条件下,其运动是一个非线性随机过程,并且在船舶航行过程中不同的海区或同一海区不同时段,其统计特性有所不同,造成船舶运动的统计特性具有一定的非平稳特性。
运动极短期预报对海上作业的各种海洋平台和船舶具有很重要的意义。根据运动历史数据,预报未来极短时间的运动,进而采取相应的措施,尤其是对作业时需要进行运动补偿或具有一定运动限制要求的设备。
近年来,国内外关于船舶运动极短期预报的研究越来越多,时间序列法在其中受到了特别多的关注,这类算法只需要寻求出历史数据中的规律,就可以得出预报值,算法计算量小且易于实现。
本文的主要工作如下:
1.首先,阐述了本文的研究背景,回顾了船舶运动极短期预报的发展历程及研究现状;比较了已有的多种预报算法,并总结出各自的优缺点。
2.首先,阐述了基于传统时间序列的AR模型船舶运动极短期预报算法,并将实船实测船舶运动时间序列用于预报,验证了算法的可行性,但是传统的时间序列预报算法的预报效果较差。
3.在传统时间序列法的基础上引入小波多分辨率分析法,将实船所测船舶运动时间序列分解为若干层近似意义上的平稳时间序列,再使用AR模型对每层的单支重构信号进行预报,最后综合每层的预报值得到原时间序列的预报值。通过小波多分辨率分析的频带划分规则确定了最优小波分解层数的选取方法;从分析各种小波基时频特性出发,通过理论分析及仿真计算的比较,确定了最优小波基函数的选取。
4.分析了经验模态分解法对非平稳信号的处理效果,针对船舶运动时间序列的非平稳性确定了其算法中相关参数的选取,运用经验模态分解法对传统时间预报算法和小波多分辨率时间序列预报算法进行算法改进。
5.通过对船舶运动时间的混沌识别,确定了嵌入维数以及时间延迟的选取,使用递推最小二乘(RLS)算法对混沌Volterra级数预报模型进行参数辨识。把这类混沌时间序列的预报算法的仿真计算结果与之前所述的算法进行对比。
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.
运动极短期预报对海上作业的各种海洋平台和船舶具有很重要的意义。根据运动历史数据,预报未来极短时间的运动,进而采取相应的措施,尤其是对作业时需要进行运动补偿或具有一定运动限制要求的设备。
近年来,国内外关于船舶运动极短期预报的研究越来越多,时间序列法在其中受到了特别多的关注,这类算法只需要寻求出历史数据中的规律,就可以得出预报值,算法计算量小且易于实现。
本文的主要工作如下:
1.首先,阐述了本文的研究背景,回顾了船舶运动极短期预报的发展历程及研究现状;比较了已有的多种预报算法,并总结出各自的优缺点。
2.首先,阐述了基于传统时间序列的AR模型船舶运动极短期预报算法,并将实船实测船舶运动时间序列用于预报,验证了算法的可行性,但是传统的时间序列预报算法的预报效果较差。
3.在传统时间序列法的基础上引入小波多分辨率分析法,将实船所测船舶运动时间序列分解为若干层近似意义上的平稳时间序列,再使用AR模型对每层的单支重构信号进行预报,最后综合每层的预报值得到原时间序列的预报值。通过小波多分辨率分析的频带划分规则确定了最优小波分解层数的选取方法;从分析各种小波基时频特性出发,通过理论分析及仿真计算的比较,确定了最优小波基函数的选取。
4.分析了经验模态分解法对非平稳信号的处理效果,针对船舶运动时间序列的非平稳性确定了其算法中相关参数的选取,运用经验模态分解法对传统时间预报算法和小波多分辨率时间序列预报算法进行算法改进。
5.通过对船舶运动时间的混沌识别,确定了嵌入维数以及时间延迟的选取,使用递推最小二乘(RLS)算法对混沌Volterra级数预报模型进行参数辨识。把这类混沌时间序列的预报算法的仿真计算结果与之前所述的算法进行对比。
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.
引文
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2.马军海.小波变换在斜拉桥结构检测分析中的应用研究[D].合肥工业大学, 2004.2
3.谢美萍,赵希人.基于投影寻踪学习的大型船舶运动极短期预报[J].船舶力学,4(4): 28～32, 2000
4.孟鸿鹰,刘贵忠.小波变换多尺度地震波形反演[J].地球物理学报,42(2) :241-248,1999
5.陈桂明,张明照,戚红雨.应用MATLAB语言处理数字信号与数字图像[M].北京:科学出版社,2000
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10.彭秀艳,赵希人,吕淑萍等.具有艏前波观测量的大型舰船姿态运动预报[J].系统仿真学报,14(6):809～814,2002
11.张家树,肖先赐.混沌时间序列的Volterra自适应预测[J].物理学报,49(3):403-408,2000
12.张家树,肖先赐.用于混沌时间序列自适应预测的一种少参数二阶Volterra滤波器[J].物理学报,50(07):1248-1254,2001
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17. Bates, M. R. ,Book, D. H. ,and Powell, F. D. ,Analog Computer Applications in Predictor Design[J], IRE Trans. On Elec.Com.,Sep.,Vol.EC-6,No.3,1957.
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20. Kalman, R. E. ,and Brcy, R. S. ,New Results in Linear Filtering and Prediction Theory[J] , J. Basic Engr. ASME Trans.,Vol.83D,95-108,1961.
21. Triantafyllou, M. ,and Bodson, M. ,Real Time Prediction of Marine Vessel Motions, Using Kalman Filtering Techniques[D]. Proc.OTC,Houston,Texas,1982
22. Yumori. I. R. Real Time Prediction of Ship Response to Ocean Waves Using Time Series Analysis[D], 1981
23. Triantafyllou, M. ,and Athans, M. ,Real Time Estimation of the Heaving and Pitching Motions of a Ship Using a Kalman Filter[J]. Proc. Oceanics 81,Boston MA,Sept,1981
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2.马军海.小波变换在斜拉桥结构检测分析中的应用研究[D].合肥工业大学, 2004.2
3.谢美萍,赵希人.基于投影寻踪学习的大型船舶运动极短期预报[J].船舶力学,4(4): 28～32, 2000
4.孟鸿鹰,刘贵忠.小波变换多尺度地震波形反演[J].地球物理学报,42(2) :241-248,1999
5.陈桂明,张明照,戚红雨.应用MATLAB语言处理数字信号与数字图像[M].北京:科学出版社,2000
6.张超,王雨,杨阳.基于小波多分辨分析的线性预测[J].通信对抗,1: 33～35,2006
7.马宏颖.船舶运动姿态极短期预报研究[J].哈尔滨工程大学学报,20(1):41～45,1999
8.谢美萍,赵希人.基于小波网的船舶运动极短期建模预报[J].船舶力学,6(2):15～21,2002
9.赵希人,彭秀艳,沈艳等.舰船运动极短期建模预报的研究现状[J].船舶工程,3:4～8,2002
10.彭秀艳,赵希人,吕淑萍等.具有艏前波观测量的大型舰船姿态运动预报[J].系统仿真学报,14(6):809～814,2002
11.张家树,肖先赐.混沌时间序列的Volterra自适应预测[J].物理学报,49(3):403-408,2000
12.张家树,肖先赐.用于混沌时间序列自适应预测的一种少参数二阶Volterra滤波器[J].物理学报,50(07):1248-1254,2001
13.吕金虎,陆军安,陈士华.混沌时间序列分析及其应用[M].武汉:武汉大学出版社,2005
14.韩敏.混沌时间序列预测理论与方法[M].北京:中国水利水电出版社,2007
15. Wiener, N., Extrapolation, Interpolation and Smoothing of Stationary Time Series[M], John Wiley and Sons,Ine.,NewYork,1949.
16. Fleck, J. T. ,Short Time Prediction of the Motion of a Ship in Waves[J],Proc. Ist Conf.On Ships and Waves,October 1954,Published by Council on wave Research and SNAME.
17. Bates, M. R. ,Book, D. H. ,and Powell, F. D. ,Analog Computer Applications in Predictor Design[J], IRE Trans. On Elec.Com.,Sep.,Vol.EC-6,No.3,1957.
18. Kaplan, P. ,and Sargent, T. P. ,Theoretical Study of the Motions of an Aircraft Carrier at Sea[J], Oceanics,Inc.Rpt.,January,No.65-22,1965.
19. Kaplan, P. ,and Ross, D. ,Comparative Performance of Wave Measuring systems Mounted on ships in Motion at sea[J], Presented as 4th National Symp. Of Marine Science Inst., ISA, Coco Beach,Florida,January 1968.
20. Kalman, R. E. ,and Brcy, R. S. ,New Results in Linear Filtering and Prediction Theory[J] , J. Basic Engr. ASME Trans.,Vol.83D,95-108,1961.
21. Triantafyllou, M. ,and Bodson, M. ,Real Time Prediction of Marine Vessel Motions, Using Kalman Filtering Techniques[D]. Proc.OTC,Houston,Texas,1982
22. Yumori. I. R. Real Time Prediction of Ship Response to Ocean Waves Using Time Series Analysis[D], 1981
23. Triantafyllou, M. ,and Athans, M. ,Real Time Estimation of the Heaving and Pitching Motions of a Ship Using a Kalman Filter[J]. Proc. Oceanics 81,Boston MA,Sept,1981
24. Lu Shu ping, Yang Xue jing, and Zhao Xi ren. the Application of Modeling and Prediction with MRA wavelet network[J]. Journal of Marine Science and Application,3(1):20～23,2004
25. Jensen J. J. and Mansour A. E. Estimation of Ship Motions using Closed-form Expressions[J]. Ocean Engineering 31(2004):61～85
26.北川源四郎著,姜兴起等译.现代时间序列分析方法及其实用计算机程序[M].大连:大连海事大学出版社,1996
27.胡广书.现代信号处理教程[M].北京:清华大学出版社,2004.11
28.樊启斌.小波分析[M].武汉:武汉大学出版社,2008.10
29.程正兴.小波分析与应用实例[M].西安:西安交通大学出版社,2006.9
30.胡昌华,李国华,刘涛,周志杰.基于MATLAB的系统分析与设计——小波分析[M].西安:西安电子科技大学出版社,2004.1
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