引入经验模态分解的舰船运动非线性极短期预报  

Extreme Short Term Prediction of Nonlinear Ship Motion by Empirical Mode Decomposition Method

作　　者：王安存[1] 段文洋[2] 姜治芳[1] 

机构地区：[1]中国舰船研究设计中心,湖北武汉430064 [2]哈尔滨工程大学船舶工程学院,黑龙江哈尔滨150001

出　　处：《舰船工程研究》2007年第4期1-5,11,共6页

摘　　要：船舶运动的极短期预报在船舶系统、设备作业等方面具有重要的意义，采用自回归模型对船舶运动进行预报等预报效果，如精度和时间长度，与实际应用的需要还存在较大距离。在自回归（AR）数学模型中引入经验模态分解（EMD）法，利用该方法将船舶运动的时历数据以“筛分”的方式分解成几个平稳的本征模态函数（IMF），并分别建立每个IMF的AR模型，用AR模型进行预报，然后将每个IMF的预报结果相加，将各预报结果的和作为原始信号的预报结果。采用该方法进行船舶非线性极短期预报对提高预报精度有一定的积极作用。The extreme short term prediction on the nonlinear ship motion may be of great interest to the deck operation and system function, and therefore could reduce the risk of accident. AR （Auto- Regress） theory is commonly used to predict the ship motion, however, the precision and time period of this prediction can not satisfy the actual needs. In this study, Empirical Mode Decomposition （EMD） method is incorporated into AR model of ship motion analysis, by this method, the time series data are processed and decomposed into several intrinsic mode functions （IMFs） and also a residual trend term. Each IMF and residual AR model and their prediction results are achieved, and the ship motion data characterized by the sum of IMFs′and residual AR model predictions are obtained. It shows that EMD method is more suited for the extreme short term prediction on the nonlinear ship motion in comparison with the AR model prediction.

关 键 词：舰船 线性运动 经验模态分解 自回归 预报 

分 类 号：U661.32[交通运输工程—船舶及航道工程]