Search

• A novel floating platform motion forecasting model is proposed. • A novel chaotic efficient bat algorithm is proposed to receive optimized parameter. • The ensemble empirical mode decomposition algorithm is proposed to decompose floating platform motion data. • The proposed hybrid kernel based support vector regression model receives higher forecasting accuracy. This paper presents a model for forecasting the motion of a floating platform with satisfactory forecasting accuracy. First, owing to the complex nonlinear characteristics of a time series of floating platform motion data, a support vector regression model with a hybrid kernel function is used to simulate the motion of a floating platform. Second, the proposed chaotic efficient bat algorithm, based on the chaotic, niche search, and evolution mechanisms, is used to optimize the parameters of the hybrid kernel-based support vector regression model. Third, the ensemble empirical mode decomposition algorithm is utilized to decompose the original floating platform motion time series into a series of intrinsic mode functions and residuals. The ultimate forecasting results are obtained by summing the outputs of these functions. Subsequently, motion data for a real floating platform are used to evaluate the reliability and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]