Research On Forecast For Roll Motion Attitude Of Large Ship

It is a concerned problem that real-time forecast of six degree of freedom for shipmotion in wave by international shipping industry and ship engineering, especially eachcountry navy. At present, time series analysis, gray system theory and neural network areusually used for forecast of ship motion, the largest advantage of which is that the law isjust sought by history data of ship motion for prediction, without knowing any priorinformation and state equation of ship navigation attitude. However, it is difficult toestablish an exact forecast model, because these data typically exhibit multi-variabledynamic evolution behavior and multi-level structure characteristics and so on. But motionseries have some laws, such as development of the period has similar or identical law to thatof the previous period. Therefore, in order to improve real-time and effective length of timeof forecast, intrinsic characteristics and evolution characteristics of ship rolling motion timeseries are analyzed from two aspects based on forecast thought of data-driven,respectively.Meantime, some kinds of forecast models are targeted designed by combiningneural network and support vector machine, which specifically includes the followingaspects:Firstly, in order to solve accurate forecast problem under poor information anduncertain conditions for traditional single prediction methods, a hybrid intelligent forecastmodel of ship motion is designed based on empirical mode decomposition. Thecharacteristics of ship rolling motion series is studied and analyzed using empirical modedecomposition method to decompose feature information. Several basic mode componentsand a remainder are reconstructed into high, middle and low frequency three componentsusing run-length method, which makes the number of forecast objects fixed. Andinformation entropy weighted Elman neural network forecast model is established for eachcomponent. The result of each component conducts weighted addition by GRNN neuralnetwork, and then forecast result is obtained.Secondly, uncertain and chaos characteristics of ship motion attitude are closely linked.Considering nonlinear and uncertainty of ship motion, chaos characteristics of ship rollingtime series is specifically analyzed under four different sea conditions. The selectionmethod of delay time and embedded dimension is discussed about phase spacereconstruction of ship rolling time series, and mutual information function method and false nearest neighbor method are used to calculate the best delay time and embedded dimension.Three dimension phase diagram of ship rolling time series is drawn about chaoticcharacteristics analysis, and characteristics and differences of three dimension phasediagram for random sequence, Lorenz mapping and roll time series are also analyzed,respectively. Meantime, saturation correlation dimension method and small datum methodare used to calculate correlation dimension, Kolmogorov entropy and the largest Lyapunovexponent. Ship rolling motion is proved to possess some chaotic dynamic characteristicsfrom two aspects of qualitative and quantitative.Then, phase space reconstruction is used to approximately recover the originalmulti-dimensional nonlinear chaotic systems aiming at chaotic characteristics of rollingmotion, and combines support vector machine, which is adaptive to solve nonlinear, smallsample and uncertainty question, to establish hybrid intelligent forecast method based onimproved support vector machine, which uses self-similar structure of attractor in differentlevels to predict. About support vector machine regression method, the main researchcontent includes: Marr wavelet nuclear function is constructed to satisfy Mercer conditionfor the problem, which common kernel function can not approach any curve on squareintegrable space in theory. Variant support vector machine is obtained by adjustingconfidence range of optimization problems and establishing relationship between parameterband optimal solution of the dual problem for optimization problem, dual problem ofwhich reduce a constraint and has more concise form. Segmented support vector machine isestablished to meet the interval-based structure risk minimization principle by designingrobust loss function, which has more robust. Improved support vector machine is designedby replace two slack variables with single slack variable to adjust error, namely single slackvariable robust waveletν support vector machine, which reduces optimization range ofdual problem and improves computing speed. A number of conclusions are proved forimprove support vector machine in terms of geometric interval-based structure riskminimization principle. About optimization of parameter combinations, multi-groupcoordination evolution adaptive chaotic particle swarm algorithm is proposed to aim atsolving poor local search ability and premature convergence of the standard particle swarmalgorithm, which can adaptively adjust itself inertia weight and learning factors to completepopulation evolutionary by chaotic initialization population strategy and multiplesubpopulations coordinated strategy.At last, real-time online forecast method of ship motion time series is studied. Chaos online least squares support vector machine real-time forecast model is proposed to dealwith the problem, which forecast model obtained by training offline without consideringdynamic characteristics of data may result in sharply decline of forecast accuracy as timegoes. The model can make training result of history data obtain good use, and completeonline update sample set, regression function and real-time prediction. Least squaressupport vector machine online modeling strategy is proposed to solve the problem, whichhyper-parameter can not automatically adjust with the change of samples. Varyingparameters least squares support vector machine online forecast method is designed byheuristic rules, which hyper-parameter of support vector machine can automatically adjustby three least squares support vector machine alternating work process. Fixed parameter offorecast model is replaced by varying parameter, which more exactly explain the variabilitypresented in the sample. The method can adjust expression of forecast model in differentvariation periods of the process, which has some adaptive adjustment capability.The results of the research have important theoretical significance and potentialapplications, some of which can be applied to time series forecast research of other areas.