CAJ | 학술논문

文章基于混沌动力系统相空间重构理论，利用关联维数法和最大Lyapunov指数法，对船舶运动时间序列的混沌特性进行了判定。并利用RBF神经网络较强的非线性映射功能，结合相空间重构理论建立了船舶运动极短期直接多步预报模型。实例预报结果表明，所建立的预报模型应用于船舶运动极短期预报取得了令人满意的预报精度，预报时间可达10 s。
문장기우혼돈동력계통상공간중구이론，이용관련유수법화최대Lyapunov지수법，대선박운동시간서렬적혼돈특성진행료판정。병이용RBF신경망락교강적비선성영사공능，결합상공간중구이론건립료선박운동겁단기직접다보예보모형。실례예보결과표명，소건립적예보모형응용우선박운동겁단기예보취득료령인만의적예보정도，예보시간가체10 s。
Based on the reconstruction of the chaotic dynamic space, the correlation dimension method and Lyapunov exponent method are applied to identify the chaos characteristic of ship motion time series. Ex-treme short-term prediction model of ship motion is established based on phase space reconstruction and RBF neural network. The test results show that this prediction model produces excellent performance, which is able to predict the ship motion satisfactorily for up to 10 seconds.