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A real-time ship roll motion prediction using wavelet transform and variable RBF network

机译：基于小波变换和可变RBF网络的船舶侧倾运动实时预测

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摘要

Real-time prediction of ship roll motion is vital for marine safety and efficiency of operations onboard the ship. However, ship roll motion is a complex time-varying nonlinear process which varies with various sailing conditions as well as time-varying environmental factors. To achieve precise real-time ship roll prediction, an ensemble prediction scheme is constructed by combining the discrete wavelet transform (DWT) method with the variable-structure radial basis function (RBF) network. The DWT is used to reduce the time-series data redundancies and carry the data information in few significant uncoupled sub-series, thus facilitate the identification and prediction by using the variable RBF networks. The variable RBF networks are used to represent time-varying dynamics with both the structure and parameters are tuned in real time. The DWT-transform-based variable RBF networks are used to represent the time-varying nonlinear dynamics of ship roll movement during ship maneuvering. The effectiveness of the proposed DWT-based real-time roll prediction scheme is demonstrated by short-term ship roll motion prediction experiments based on the actual ship roll motion measurements collected during sea test of M.V. YuKun.
机译：船舶侧倾运动的实时预测对于海上安全和船上作业效率至关重要。但是，船舶侧倾运动是一个复杂的时变非线性过程，它随各种航行条件以及时变环境因素而变化。为了实现精确的实时船舶侧倾预测，通过将离散小波变换（DWT）方法与可变结构径向基函数（RBF）网络相结合，构造了整体预测方案。 DWT用于减少时间序列的数据冗余，并在很少的重要非耦合子序列中携带数据信息，从而通过使用可变RBF网络方便进行识别和预测。可变的RBF网络用于表示时变动力学，其结构和参数都可以实时调整。基于DWT变换的可变RBF网络用于表示船舶操纵过程中船舶侧倾运动的时变非线性动力学。基于MWT海上测试期间收集的实际船舶侧倾运动测量值的短期船舶侧倾运动预测实验证明了基于DWT的实时侧倾预测方案的有效性。于坤

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