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

Abstract

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.