A Hybrid BPNN-GARF-SVR Prediction Model Based on EEMD for Ship Motion

作     者：Hao Han Wei Wang 

作者机构：School of Instrumentation and Optoelectronic EngineeringBeihang UniversityBeijing100191China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第134卷第2期

页      面：1353-1370页

核心收录：

学科分类：08[工学] 082402[工学-轮机工程] 0824[工学-船舶与海洋工程] 

主　　题：Back propagation neural network ensemble empirical mode decomposition genetic algorithm random forest SVR ship motion prediction 

摘      要：Accurate prediction of shipmotion is very important for ensuringmarine safety,weapon control,and aircraft carrier landing,*** motion is a complex time-varying nonlinear process which is affected by many *** series analysis method and many machine learning methods such as neural networks,support vector machines regression(SVR)have been widely used in ship motion ***,these single models have certain limitations,so this paper adopts amulti-model prediction ***,ensemble empirical mode decomposition(EEMD)is used to remove noise in ship motion *** the randomforest(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 *** the weights coefficients are determined by the correlation coefficients,reducing the risk of prediction and improving the *** experimental results show that the proposed combined model EEMD-GARF-BPNN-SVR is superior to the single predictive model and more *** mean absolute percentage error(MAPE)of the proposed model is 0.84%,but the results of the single models are greater than 1%.