Online ship roll motion prediction based on grey sequential extreme learning machine

Abstract

For the online prediction of nonlinear systems with characteristics of time-varying dynamics and uncertainty, a sequential grey prediction approach is proposed based on the online sequential extreme learning machine (OS-ELM). The grey processing of time series alleviates the unfavorable effects of uncertainty in measurement data; the extremely fast learning speed and high generalization accuracy of OS-ELM enable online application of the sequential grey prediction approach. Ship's roll motion at sea is a complex nonlinear process with time-varying dynamics. Its dynamics also involves uncertainty caused by wind, random waves and rudder actions. In this paper, the proposed OS-ELM-based grey prediction approach is implemented for online ship roll prediction. The simulation of prediction is based on measurement data obtained from sea trials of the scientific research and training ship Yu Kun. Simulation results of ship roll prediction demonstrate the effectiveness and efficiency of the proposed grey neural prediction approach in dealing with time-varying nonlinear system with uncertainty.