Open Access
ARTICLE
Abstract
Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier
landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time
series analysis method and many machine learning methods such as neural networks, support vector machines
regression (SVR) have been widely used in ship motion predictions. However, these single models have certain
limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition
(EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction model optimized by
genetic algorithm (GA), back propagation neural network (BPNN) prediction model and SVR prediction model
are respectively established, and the final prediction results are obtained by results of three models. And the
weights coefficients are determined by the correlation coefficients, reducing the risk of prediction and improving
the reliability. The experimental results show that the proposed combined model EEMD-GARF-BPNN-SVR is
superior to the single predictive model and more reliable. The mean absolute percentage error (MAPE) of the
proposed model is 0.84%, but the results of the single models are greater than 1%.
Keywords
Cite This Article
APA Style
Han, H., Wang, W. (2023). A hybrid BPNN-GARF-SVR prediction model based on EEMD for ship motion. Computer Modeling in Engineering & Sciences, 134(2), 1353-1370. https://doi.org/10.32604/cmes.2022.021494
Vancouver Style
Han H, Wang W. A hybrid BPNN-GARF-SVR prediction model based on EEMD for ship motion. Comput Model Eng Sci. 2023;134(2):1353-1370 https://doi.org/10.32604/cmes.2022.021494
IEEE Style
H. Han and W. Wang, "A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion," Comput. Model. Eng. Sci., vol. 134, no. 2, pp. 1353-1370. 2023. https://doi.org/10.32604/cmes.2022.021494